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Laswick Z, Wu X, Surendran A, Zhou Z, Ji X, Matrone GM, Leong WL, Rivnay J. Tunable anti-ambipolar vertical bilayer organic electrochemical transistor enable neuromorphic retinal pathway. Nat Commun 2024; 15:6309. [PMID: 39060249 PMCID: PMC11282299 DOI: 10.1038/s41467-024-50496-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024] Open
Abstract
Increasing demand for bio-interfaced human-machine interfaces propels the development of organic neuromorphic electronics with small form factors leveraging both ionic and electronic processes. Ion-based organic electrochemical transistors (OECTs) showing anti-ambipolarity (OFF-ON-OFF states) reduce the complexity and size of bio-realistic Hodgkin-Huxley(HH) spiking circuits and logic circuits. However, limited stable anti-ambipolar organic materials prevent the design of integrated, tunable, and multifunctional neuromorphic and logic-based systems. In this work, a general approach for tuning anti-ambipolar characteristics is presented through assembly of a p-n bilayer in a vertical OECT (vOECT) architecture. The vertical OECT design reduces device footprint, while the bilayer material tuning controls the anti-ambipolarity characteristics, allowing control of the device's on and off threshold voltages, and peak position, while reducing size thereby enabling tunable threshold spiking neurons and logic gates. Combining these components, a mimic of the retinal pathway reproducing the wavelength and light intensity encoding of horizontal cells to spiking retinal ganglion cells is demonstrated. This work enables further incorporation of conformable and adaptive OECT electronics into biointegrated devices featuring sensory coding through parallel processing for diverse artificial intelligence and computing applications.
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Affiliation(s)
- Zachary Laswick
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
| | - Xihu Wu
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Abhijith Surendran
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
| | - Zhongliang Zhou
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Xudong Ji
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
| | | | - Wei Lin Leong
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
| | - Jonathan Rivnay
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA.
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA.
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2
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Zhao K, He D, Liu X, Ren F, Wang J, Yan Y, Huang M, Wang Y, Zhang X. Enhance Carrier Diffusion of Monolayer MoSe 2 by Interface Engineering. ACS APPLIED MATERIALS & INTERFACES 2024; 16:34349-34357. [PMID: 38912925 DOI: 10.1021/acsami.4c05143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
Two-dimensional materials hold great potentials for beyond-CMOS (complementary metal-oxide-semiconductor) electronical and optoelectrical applications, and the development of field effect transistors (FET) with excellent performance using such materials is of particular interest. How to improve the performance of devices thus becomes an urgent issue. The performance of FETs depends greatly on the intrinsic electrical properties of the channel materials, meanwhile the device interface quality, such as extrinsic scattering of charged impurities, charge traps, and substrate surface roughness have a great influence on the performance. In this paper, the impact of the interface quality on the carrier diffusion behaviors of monolayer (ML) MoSe2 has been investigated by using an in situ ultrafast laser technique to avoid the surface contamination during device fabrication process. Two types of self-assembled monolayers (SAMs) are introduced to modify the gate dielectric surface through an interface engineering approach to obtain chemical-stable interfaces. The results showed that the transport properties of ML MoSe2 were enhanced after interface engineering, for example, the carrier mobility of ML MoSe2 was improved from ∼59.4 to ∼166.5 cm2 V-1 s-1 after the SAM modification. Meanwhile, the photocarrier dynamics of ML MoSe2 before and after interfacial engineering were also carefully studied. Our studies provide a feasible method for improving the carrier diffusion behaviors of such materials, and making them suited for application in future integrated circuit.
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Affiliation(s)
- Kun Zhao
- Key Laboratory of Luminescence and Optical Information, Ministry of Education, Institute of Optoelectronic Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Dawei He
- Key Laboratory of Luminescence and Optical Information, Ministry of Education, Institute of Optoelectronic Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Xiaojing Liu
- Key Laboratory of Luminescence and Optical Information, Ministry of Education, Institute of Optoelectronic Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Fangying Ren
- Key Laboratory of Luminescence and Optical Information, Ministry of Education, Institute of Optoelectronic Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Jiarong Wang
- Key Laboratory of Luminescence and Optical Information, Ministry of Education, Institute of Optoelectronic Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Yige Yan
- Key Laboratory of Luminescence and Optical Information, Ministry of Education, Institute of Optoelectronic Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Mohan Huang
- Department of Optical Engineering, Zhejiang A&F University, Linan 311300, P. R. China
| | - Yongsheng Wang
- Key Laboratory of Luminescence and Optical Information, Ministry of Education, Institute of Optoelectronic Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Xiaoxian Zhang
- Key Laboratory of Luminescence and Optical Information, Ministry of Education, Institute of Optoelectronic Technology, Beijing Jiaotong University, Beijing 100044, China
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3
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Meng Y, Wang W, Wang W, Li B, Zhang Y, Ho J. Anti-Ambipolar Heterojunctions: Materials, Devices, and Circuits. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2306290. [PMID: 37580311 DOI: 10.1002/adma.202306290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/31/2023] [Indexed: 08/16/2023]
Abstract
Anti-ambipolar heterojunctions are vital in constructing high-frequency oscillators, fast switches, and multivalued logic (MVL) devices, which hold promising potential for next-generation integrated circuit chips and telecommunication technologies. Thanks to the strategic material design and device integration, anti-ambipolar heterojunctions have demonstrated unparalleled device and circuit performance that surpasses other semiconducting material systems. This review aims to provide a comprehensive summary of the achievements in the field of anti-ambipolar heterojunctions. First, the fundamental operating mechanisms of anti-ambipolar devices are discussed. After that, potential materials used in anti-ambipolar devices are discussed with particular attention to 2D-based, 1D-based, and organic-based heterojunctions. Next, the primary device applications employing anti-ambipolar heterojunctions, including anti-ambipolar transistors (AATs), photodetectors, frequency doublers, and synaptic devices, are summarized. Furthermore, alongside the advancements in individual devices, the practical integration of these devices at the circuit level, including topics such as MVL circuits, complex logic gates, and spiking neuron circuits, is also discussed. Lastly, the present key challenges and future research directions concerning anti-ambipolar heterojunctions and their applications are also emphasized.
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Affiliation(s)
- You Meng
- Department of Materials Science and Engineering, State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon, Hong Kong SAR, 999077, China
| | - Weijun Wang
- Department of Materials Science and Engineering, State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon, Hong Kong SAR, 999077, China
| | - Wei Wang
- Department of Materials Science and Engineering, State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon, Hong Kong SAR, 999077, China
| | - Bowen Li
- Department of Materials Science and Engineering, State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon, Hong Kong SAR, 999077, China
| | - Yuxuan Zhang
- Department of Materials Science and Engineering, State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon, Hong Kong SAR, 999077, China
| | - Johnny Ho
- Department of Materials Science and Engineering, State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon, Hong Kong SAR, 999077, China
- Institute for Materials Chemistry and Engineering, Kyushu University, Fukuoka, 816-8580, Japan
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4
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Fan ZY, Tang Z, Fang JL, Jiang YP, Liu QX, Tang XG, Zhou YC, Gao J. Neuromorphic Computing of Optoelectronic Artificial BFCO/AZO Heterostructure Memristors Synapses. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:583. [PMID: 38607116 PMCID: PMC11013421 DOI: 10.3390/nano14070583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/19/2024] [Accepted: 03/25/2024] [Indexed: 04/13/2024]
Abstract
Compared with purely electrical neuromorphic devices, those stimulated by optical signals have gained increasing attention due to their realistic sensory simulation. In this work, an optoelectronic neuromorphic device based on a photoelectric memristor with a Bi2FeCrO6/Al-doped ZnO (BFCO/AZO) heterostructure is fabricated that can respond to both electrical and optical signals and successfully simulate a variety of synaptic behaviors, such as STP, LTP, and PPF. In addition, the photomemory mechanism was identified by analyzing the energy band structures of AZO and BFCO. A convolutional neural network (CNN) architecture for pattern classification at the Mixed National Institute of Standards and Technology (MNIST) was used and improved the recognition accuracy of the MNIST and Fashion-MNIST datasets to 95.21% and 74.19%, respectively, by implementing an improved stochastic adaptive algorithm. These results provide a feasible approach for future implementation of optoelectronic synapses.
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Affiliation(s)
- Zhao-Yuan Fan
- School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; (Z.-Y.F.)
| | - Zhenhua Tang
- School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; (Z.-Y.F.)
| | - Jun-Lin Fang
- School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; (Z.-Y.F.)
| | - Yan-Ping Jiang
- School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; (Z.-Y.F.)
| | - Qiu-Xiang Liu
- School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; (Z.-Y.F.)
| | - Xin-Gui Tang
- School of Physics and Optoelectric Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; (Z.-Y.F.)
| | - Yi-Chun Zhou
- School of Advanced Materials and Nanotechnology, Xidian University, Xi’an 710126, China
| | - Ju Gao
- Department of Physics, The University of Hong Kong, Hong Kong 999077, China
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5
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Wan C, Pei M, Shi K, Cui H, Long H, Qiao L, Xing Q, Wan Q. Toward a Brain-Neuromorphics Interface. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2311288. [PMID: 38339866 DOI: 10.1002/adma.202311288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/17/2024] [Indexed: 02/12/2024]
Abstract
Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.
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Affiliation(s)
- Changjin Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Mengjiao Pei
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Kailu Shi
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Hangyuan Cui
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Haotian Long
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Lesheng Qiao
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qianye Xing
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qing Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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Zheng Y, Ghosh S, Das S. A Butterfly-Inspired Multisensory Neuromorphic Platform for Integration of Visual and Chemical Cues. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023:e2307380. [PMID: 38069632 DOI: 10.1002/adma.202307380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 11/25/2023] [Indexed: 12/23/2023]
Abstract
Unisensory cues are often insufficient for animals to effectively engage in foraging, mating, and predatory activities. In contrast, integration of cues collected from multiple sensory organs enhances the overall perceptual experience and thereby facilitates better decision-making. Despite the importance of multisensory integration in animals, the field of artificial intelligence (AI) and neuromorphic computing has primarily focused on processing unisensory information. This lack of emphasis on multisensory integration can be attributed to the absence of a miniaturized hardware platform capable of co-locating multiple sensing modalities and enabling in-sensor and near-sensor processing. In this study, this limitation is addressed by utilizing the chemo-sensing properties of graphene and the photo-sensing capability of monolayer molybdenum disulfide (MoS2 ) to create a multisensory platform for visuochemical integration. Additionally, the in-memory-compute capability of MoS2 memtransistors is leveraged to develop neural circuits that facilitate multisensory decision-making. The visuochemical integration platform is inspired by intricate courtship of Heliconius butterflies, where female species rely on the integration of visual cues (such as wing color) and chemical cues (such as pheromones) generated by the male butterflies for mate selection. The butterfly-inspired visuochemical integration platform has significant implications in both robotics and the advancement of neuromorphic computing, going beyond unisensory intelligence and information processing.
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Affiliation(s)
- Yikai Zheng
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Subir Ghosh
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
- Electrical Engineering, Penn State University, University Park, PA, 16802, USA
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- Materials Research Institute, Penn State University, University Park, PA, 16802, USA
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7
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Studholme SJ, Heywood ZE, Mallinson JB, Steel JK, Bones PJ, Arnold MD, Brown SA. Computation via Neuron-like Spiking in Percolating Networks of Nanoparticles. NANO LETTERS 2023; 23:10594-10599. [PMID: 37955398 DOI: 10.1021/acs.nanolett.3c03551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2023]
Abstract
The biological brain is a highly efficient computational system in which information processing is performed via electrical spikes. Neuromorphic computing systems that work on similar principles could support the development of the next generation of artificial intelligence and, in particular, enable low-power edge computing. Percolating networks of nanoparticles (PNNs) have previously been shown to exhibit critical spiking behavior, with promise for highly efficient natural computation. Here we employ a rate coding scheme to show that PNNs can perform Boolean operations and image classification. Near perfect accuracy is achieved in both tasks by manipulating the spiking activity using certain control voltages. We demonstrate that the key to successful computation is that nanoscale tunnel gaps within the percolating networks transform input data through a powerful modulus-like nonlinearity. These results provide a basis for implementation of further computational schemes that exploit the brain-like criticality of these networks.
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Affiliation(s)
- Sofie J Studholme
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matu̅, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - Zachary E Heywood
- Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - Joshua B Mallinson
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matu̅, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - Jamie K Steel
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matu̅, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - Philip J Bones
- Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - Matthew D Arnold
- School of Mathematical and Physical Sciences, University of Technology Sydney, PO Box 123, Broadway NSW 2007, Australia
| | - Simon A Brown
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matu̅, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
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Parashar RK, Kandpal S, Pal N, Manna D, Pal BN, Kumar R, Mondal PC. Coexistence of Electrochromism and Bipolar Nonvolatile Memory in a Single Viologen. ACS APPLIED MATERIALS & INTERFACES 2023. [PMID: 37883131 DOI: 10.1021/acsami.3c12489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
Viologens are fascinating redox-active organic compounds that have been widely explored in electrochromic devices (ECDs). However, the combination of electrochromic and resistive random-access memory in a single viologen remains unexplored. We report the coexistence of bistate electrochromic and single-resistor (1R) memory functions in a novel viologen. A high-performance electrochromic function is achieved by combining viologen (BzV2+2PF6) with polythiophene (P3HT), enabling a "push-pull" electronic effect due to the efficient intermolecular charge transfer in response to an applied bias. The ECDs show high coloration efficiency (ca. 1150 ± 10 cm2 C-1), subsecond switching time, good cycle stability (>103 switching cycles), and low-bias operation (±1.5 V). The ECDs require low power for switching the color states (55 μW cm-2 for magenta and 141 μW cm-2 for blue color). The random-access memory devices (p+2-Si/BzV2+2PF6/Al) exhibit distinct low and high resistive states with an ON/OFF ratio of ∼103, bipolar and nonvolatile characteristics that manifest good performances, and "Write"-"Read"-"Erase" (WRE) functions. The charge conduction mechanism of the RRAM device is elucidated by the Poole-Frenkel model where SET and RESET states arise at a low transition voltage (VT = ±1.7 V). Device statistics and performance parameters for both electrochromic and memory devices are compared with the literature data. Our findings on electrochromism and nonvolatile memory originated in the same viologen could boost the development of multifunctional, smart, wearable, flexible, and low-cost optoelectronic devices.
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Affiliation(s)
- Ranjeev Kumar Parashar
- Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur 208016, Uttar Pradesh, India
| | - Suchita Kandpal
- Department of Physics, Indian Institute of Technology Indore, Simrol 453552, India
| | - Nila Pal
- Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur 208016, Uttar Pradesh, India
- School of Materials Science and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi 221005, India
| | - Debashree Manna
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Flemingovo námĕstí 542/2, 160 00 Prague, Czech Republic
| | - Bhola Nath Pal
- School of Materials Science and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi 221005, India
| | - Rajesh Kumar
- Department of Physics, Indian Institute of Technology Indore, Simrol 453552, India
| | - Prakash Chandra Mondal
- Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur 208016, Uttar Pradesh, India
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9
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Oberoi A, Han Y, Stepanoff SP, Pannone A, Sun Y, Lin YC, Chen C, Shallenberger JR, Zhou D, Terrones M, Redwing JM, Robinson JA, Wolfe DE, Yang Y, Das S. Toward High-Performance p-Type Two-Dimensional Field Effect Transistors: Contact Engineering, Scaling, and Doping. ACS NANO 2023; 17:19709-19723. [PMID: 37812500 DOI: 10.1021/acsnano.3c03060] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Abstract
n-type field effect transistors (FETs) based on two-dimensional (2D) transition-metal dichalcogenides (TMDs) such as MoS2 and WS2 have come close to meeting the requirements set forth in the International Roadmap for Devices and Systems (IRDS). However, p-type 2D FETs are dramatically lagging behind in meeting performance standards. Here, we adopt a three-pronged approach that includes contact engineering, channel length (Lch) scaling, and monolayer doping to achieve high performance p-type FETs based on synthetic WSe2. Using electrical measurements backed by atomistic imaging and rigorous analysis, Pd was identified as the favorable contact metal for WSe2 owing to better epitaxy, larger grain size, and higher compressive strain, leading to a lower Schottky barrier height. While the ON-state performance of Pd-contacted WSe2 FETs was improved by ∼10× by aggressively scaling Lch from 1 μm down to ∼20 nm, ultrascaled FETs were found to be contact limited. To reduce the contact resistance, monolayer tungsten oxyselenide (WOxSey) obtained using self-limiting oxidation of bilayer WSe2 was used as a p-type dopant. This led to ∼5× improvement in the ON-state performance and ∼9× reduction in the contact resistance. We were able to achieve a median ON-state current as high as ∼10 μA/μm for ultrascaled and doped p-type WSe2 FETs with Pd contacts. We also show the applicability of our monolayer doping strategy to other 2D materials such as MoS2, MoTe2, and MoSe2.
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Affiliation(s)
- Aaryan Oberoi
- Department of Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Ying Han
- Department of Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Sergei P Stepanoff
- Department of Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Applied Research Laboratory, Penn State University, University Park, Pennsylvania 16802, United States
| | - Andrew Pannone
- Department of Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Yongwen Sun
- Department of Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Yu-Chuan Lin
- Department of Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Materials Science and Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu City 300093, Taiwan
| | - Chen Chen
- 2D Crystal Consortium Materials Innovation Platform, Penn State University, University Park, Pennsylvania 16802, United States
| | - Jeffrey R Shallenberger
- Materials Characterization Laboratory, Penn State University, University Park, Pennsylvania 16802, United States
| | - Da Zhou
- Department of Physics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Mauricio Terrones
- Department of Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Physics, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, United States
| | - Joan M Redwing
- Department of Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- 2D Crystal Consortium Materials Innovation Platform, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Electrical Engineering, Penn State University, University Park, Pennsylvania 16802, United States
| | - Joshua A Robinson
- Department of Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- 2D Crystal Consortium Materials Innovation Platform, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Physics, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, United States
| | - Douglas E Wolfe
- Department of Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Applied Research Laboratory, Penn State University, University Park, Pennsylvania 16802, United States
| | - Yang Yang
- Department of Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Saptarshi Das
- Department of Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Electrical Engineering, Penn State University, University Park, Pennsylvania 16802, United States
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10
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Fan S, Wu E, Cao M, Xu T, Liu T, Yang L, Su J, Liu J. Flexible In-Ga-Zn-N-O synaptic transistors for ultralow-power neuromorphic computing and EEG-based brain-computer interfaces. MATERIALS HORIZONS 2023; 10:4317-4328. [PMID: 37431592 DOI: 10.1039/d3mh00759f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
Designing low-power and flexible artificial neural devices with artificial neural networks is a promising avenue for creating brain-computer interfaces (BCIs). Herein, we report the development of flexible In-Ga-Zn-N-O synaptic transistors (FISTs) that can simulate essential and advanced biological neural functions. These FISTs are optimized to achieve ultra-low power consumption under a super-low or even zero channel bias, making them suitable for wearable BCI applications. The effective tunability of synaptic behaviors promotes the realization of associative and non-associative learning, facilitating Covid-19 chest CT edge detection. Importantly, FISTs exhibit high tolerance to long-term exposure under an ambient environment and bending deformation, indicating their suitability for wearable BCI systems. We demonstrate that an array of FISTs can classify vision-evoked EEG signals with up to ∼87.9% and 94.8% recognition accuracy for EMNIST-Digits and MindBigdata, respectively. Thus, FISTs have enormous potential to significantly impact the development of various BCI techniques.
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Affiliation(s)
- Shuangqing Fan
- College of Electronics and Information, Qingdao University, Qingdao 266071, China.
| | - Enxiu Wu
- State Key Laboratory of Precision Measurement Technology and Instruments, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China.
| | - Minghui Cao
- College of Electronics and Information, Qingdao University, Qingdao 266071, China.
| | - Ting Xu
- State Key Laboratory of Precision Measurement Technology and Instruments, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China.
| | - Tong Liu
- State Key Laboratory of Precision Measurement Technology and Instruments, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China.
| | - Lijun Yang
- Key Laboratory of Radiopharmacokinetics for Innovative Drugs, Chinese Academy of Medical Sciences, Tianjin Key Laboratory of Radiation Medicine and Molecular Nuclear Medicine, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, P. R. China.
| | - Jie Su
- College of Electronics and Information, Qingdao University, Qingdao 266071, China.
| | - Jing Liu
- State Key Laboratory of Precision Measurement Technology and Instruments, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China.
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11
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Ghosh S, Pannone A, Sen D, Wali A, Ravichandran H, Das S. An all 2D bio-inspired gustatory circuit for mimicking physiology and psychology of feeding behavior. Nat Commun 2023; 14:6021. [PMID: 37758750 PMCID: PMC10533903 DOI: 10.1038/s41467-023-41046-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 08/21/2023] [Indexed: 09/29/2023] Open
Abstract
Animal behavior involves complex interactions between physiology and psychology. However, most AI systems neglect psychological factors in decision-making due to a limited understanding of the physiological-psychological connection at the neuronal level. Recent advancements in brain imaging and genetics have uncovered specific neural circuits that regulate behaviors like feeding. By developing neuro-mimetic circuits that incorporate both physiology and psychology, a new emotional-AI paradigm can be established that bridges the gap between humans and machines. This study presents a bio-inspired gustatory circuit that mimics adaptive feeding behavior in humans, considering both physiological states (hunger) and psychological states (appetite). Graphene-based chemitransistors serve as artificial gustatory taste receptors, forming an electronic tongue, while 1L-MoS2 memtransistors construct an electronic-gustatory-cortex comprising a hunger neuron, appetite neuron, and feeding circuit. This work proposes a novel paradigm for emotional neuromorphic systems with broad implications for human health. The concept of gustatory emotional intelligence can extend to other sensory systems, benefiting future humanoid AI.
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Affiliation(s)
- Subir Ghosh
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Andrew Pannone
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Dipanjan Sen
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Akshay Wali
- Electrical Engineering, Penn State University, University Park, PA, 16802, USA
| | | | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA.
- Electrical Engineering, Penn State University, University Park, PA, 16802, USA.
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA.
- Materials Research Institute, Penn State University, University Park, PA, 16802, USA.
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12
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Sadaf MUK, Sakib NU, Pannone A, Ravichandran H, Das S. A bio-inspired visuotactile neuron for multisensory integration. Nat Commun 2023; 14:5729. [PMID: 37714853 PMCID: PMC10504285 DOI: 10.1038/s41467-023-40686-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/03/2023] [Indexed: 09/17/2023] Open
Abstract
Multisensory integration is a salient feature of the brain which enables better and faster responses in comparison to unisensory integration, especially when the unisensory cues are weak. Specialized neurons that receive convergent input from two or more sensory modalities are responsible for such multisensory integration. Solid-state devices that can emulate the response of these multisensory neurons can advance neuromorphic computing and bridge the gap between artificial and natural intelligence. Here, we introduce an artificial visuotactile neuron based on the integration of a photosensitive monolayer MoS2 memtransistor and a triboelectric tactile sensor which minutely captures the three essential features of multisensory integration, namely, super-additive response, inverse effectiveness effect, and temporal congruency. We have also realized a circuit which can encode visuotactile information into digital spiking events, with probability of spiking determined by the strength of the visual and tactile cues. We believe that our comprehensive demonstration of bio-inspired and multisensory visuotactile neuron and spike encoding circuitry will advance the field of neuromorphic computing, which has thus far primarily focused on unisensory intelligence and information processing.
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Affiliation(s)
| | - Najam U Sakib
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Andrew Pannone
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | | | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA.
- Electrical Engineering, Penn State University, University Park, PA, 16802, USA.
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA.
- Materials Research Institute, Penn State University, University Park, PA, 16802, USA.
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13
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Wali A, Das S. Hardware and Information Security Primitives Based on 2D Materials and Devices. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205365. [PMID: 36564174 DOI: 10.1002/adma.202205365] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 12/01/2022] [Indexed: 05/05/2023]
Abstract
Hardware security is a major concern for the entire semiconductor ecosystem that accounts for billions of dollars in annual losses. Similarly, information security is a critical need for the rapidly proliferating edge devices that continuously collect and communicate a massive volume of data. While silicon-based complementary metal-oxide-semiconductor technology offers security solutions, these are largely inadequate, inefficient, and often inconclusive, as well as resource intensive in time, energy, and cost, leading to tremendous room for innovation in this field. Furthermore, silicon-based security primitives have shown vulnerability to machine learning (ML) attacks. In recent years, 2D materials such as graphene and transition metal dichalcogenides have been intensely explored to mitigate these security challenges. In this review, 2D-materials-based hardware security solutions such as camouflaging, true random number generation, watermarking, anticounterfeiting, physically unclonable functions, and logic locking of integrated circuits (ICs) are summarized with accompanying discussion on their reliability and resilience to ML attacks. In addition, the role of native defects in 2D materials in developing high entropy hardware security primitives is also examined. Finally, the existing challenges for 2D materials, which must be overcome for large-scale deployment of 2D ICs to meet the security needs of the semiconductor industry, are discussed.
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Affiliation(s)
- Akshay Wali
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, 16802, USA
| | - Saptarshi Das
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, 16802, USA
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- Materials Research Institute, Penn State University, University Park, PA, 16802, USA
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14
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Zhang F, Li C, Li Z, Dong L, Zhao J. Recent progress in three-terminal artificial synapses based on 2D materials: from mechanisms to applications. MICROSYSTEMS & NANOENGINEERING 2023; 9:16. [PMID: 36817330 PMCID: PMC9935897 DOI: 10.1038/s41378-023-00487-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/17/2022] [Accepted: 01/03/2023] [Indexed: 06/18/2023]
Abstract
Synapses are essential for the transmission of neural signals. Synaptic plasticity allows for changes in synaptic strength, enabling the brain to learn from experience. With the rapid development of neuromorphic electronics, tremendous efforts have been devoted to designing and fabricating electronic devices that can mimic synapse operating modes. This growing interest in the field will provide unprecedented opportunities for new hardware architectures for artificial intelligence. In this review, we focus on research of three-terminal artificial synapses based on two-dimensional (2D) materials regulated by electrical, optical and mechanical stimulation. In addition, we systematically summarize artificial synapse applications in various sensory systems, including bioplastic bionics, logical transformation, associative learning, image recognition, and multimodal pattern recognition. Finally, the current challenges and future perspectives involving integration, power consumption and functionality are outlined.
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Affiliation(s)
- Fanqing Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| | - Chunyang Li
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| | - Zhongyi Li
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
| | - Lixin Dong
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon Tong, 999077 Hong Kong, China
| | - Jing Zhao
- School of Mechatronical Engineering, Beijing Institute of Technology, 100081 Beijing, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, 100081 Beijing, China
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15
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Dodda A, Jayachandran D, Subbulakshmi Radhakrishnan S, Pannone A, Zhang Y, Trainor N, Redwing JM, Das S. Bioinspired and Low-Power 2D Machine Vision with Adaptive Machine Learning and Forgetting. ACS NANO 2022; 16:20010-20020. [PMID: 36305614 DOI: 10.1021/acsnano.2c02906] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Natural intelligence has many dimensions, with some of its most important manifestations being tied to learning about the environment and making behavioral changes. In primates, vision plays a critical role in learning. The underlying biological neural networks contain specialized neurons and synapses which not only sense and process visual stimuli but also learn and adapt with remarkable energy efficiency. Forgetting also plays an active role in learning. Mimicking the adaptive neurobiological mechanisms for seeing, learning, and forgetting can, therefore, accelerate the development of artificial intelligence (AI) and bridge the massive energy gap that exists between AI and biological intelligence. Here, we demonstrate a bioinspired machine vision system based on a 2D phototransistor array fabricated from large-area monolayer molybdenum disulfide (MoS2) and integrated with an analog, nonvolatile, and programmable memory gate-stack; this architecture not only enables dynamic learning and relearning from visual stimuli but also offers learning adaptability under noisy illumination conditions at miniscule energy expenditure. In short, our demonstrated "all-in-one" hardware vision platform combines "sensing", "computing", and "storage" to not only overcome the von Neumann bottleneck of conventional complementary metal-oxide-semiconductor (CMOS) technology but also to eliminate the need for peripheral circuits and sensors.
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Affiliation(s)
- Akhil Dodda
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Darsith Jayachandran
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | | | - Andrew Pannone
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Yikai Zhang
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Nicholas Trainor
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
| | - Joan M Redwing
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Materials Research Institute, Penn State University, University Park, Pennsylvania 16802, United States
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Materials Research Institute, Penn State University, University Park, Pennsylvania 16802, United States
- Electrical Engineering and Computer Science, Penn State University, University Park, Pennsylvania 16802, United States
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16
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Subbulakshmi Radhakrishnan S, Dodda A, Das S. An All-in-One Bioinspired Neural Network. ACS NANO 2022; 16:20100-20115. [PMID: 36378680 DOI: 10.1021/acsnano.2c02172] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
In spite of recent advancements in artificial neural networks (ANNs), the energy efficiency, multifunctionality, adaptability, and integrated nature of biological neural networks remain largely unimitated by hardware neuromorphic computing systems. Here, we exploit optoelectronic, computing, and programmable memory devices based on emerging two-dimensional (2D) layered materials such as MoS2 to demonstrate a monolithically integrated, multipixel, and "all-in-one" bioinspired neural network (BNN) capable of sensing, encoding, learning, forgetting, and inferring at minuscule energy expenditure. We also demonstrate learning adaptability and simulate learning challenges under specific synaptic conditions to mimic biological learning. Our findings highlight the potential of in-memory computing and sensing based on emerging 2D materials, devices, and integrated circuits to not only overcome the bottleneck of von Neumann computing in conventional CMOS designs but also to aid in eliminating the peripheral components necessary for competing technologies such as memristors.
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Affiliation(s)
- Shiva Subbulakshmi Radhakrishnan
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania16802, United States
| | - Akhil Dodda
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania16802, United States
| | - Saptarshi Das
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania16802, United States
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania16802, United States
- Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania16802, United States
- Department of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, Pennsylvania16802, United States
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17
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Zhang L, Zhang Y, Liu F, Chen Q, Lian Y, Ma Q. On-Chip Photonic Synapses with All-Optical Memory and Neural Network Computation. MICROMACHINES 2022; 14:74. [PMID: 36677135 PMCID: PMC9862829 DOI: 10.3390/mi14010074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/22/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
Inspired by the human brain, neural network computing was expected to break the bottleneck of traditional computing, but the integrated design still faces great challenges. Here, a readily integrated membrane-system photonic synapse was demonstrated. By pre-pulse training at 1064 nm (cutoff wavelength), the photonic synapse can be regulated both excitatory and inhibitory at tunable wavelengths (1200-2000 nm). Furthermore, more weights and memory functions were shown through the photonic synapse integrated network. Additionally, the digital recognition function of the single-layer perceptron neural network constructed by photonic synapses has been successfully demonstrated. Most of the biological synaptic functions were realized by the photonic synaptic network, and it had the advantages of compact structure, scalable, adjustable wavelength, and so on, which opens up a new idea for the study of the neural synaptic network.
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Affiliation(s)
- Lulu Zhang
- MOE Key Laboratory of Trans-Scale Laser Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Engineering Research Center of Laser Technology, Beijing University of Technology, Beijing 100124, China
- Institute of Laser Engineering, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
| | - Yongzhi Zhang
- MOE Key Laboratory of Trans-Scale Laser Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Engineering Research Center of Laser Technology, Beijing University of Technology, Beijing 100124, China
- Institute of Laser Engineering, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
| | - Furong Liu
- MOE Key Laboratory of Trans-Scale Laser Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Engineering Research Center of Laser Technology, Beijing University of Technology, Beijing 100124, China
- Institute of Laser Engineering, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
| | - Qingyuan Chen
- MOE Key Laboratory of Trans-Scale Laser Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Engineering Research Center of Laser Technology, Beijing University of Technology, Beijing 100124, China
- Institute of Laser Engineering, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
| | - Yangbo Lian
- MOE Key Laboratory of Trans-Scale Laser Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Engineering Research Center of Laser Technology, Beijing University of Technology, Beijing 100124, China
- Institute of Laser Engineering, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
| | - Quanlong Ma
- MOE Key Laboratory of Trans-Scale Laser Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
- Beijing Engineering Research Center of Laser Technology, Beijing University of Technology, Beijing 100124, China
- Institute of Laser Engineering, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
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18
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Lei Y, Zhang T, Lin YC, Granzier-Nakajima T, Bepete G, Kowalczyk DA, Lin Z, Zhou D, Schranghamer TF, Dodda A, Sebastian A, Chen Y, Liu Y, Pourtois G, Kempa TJ, Schuler B, Edmonds MT, Quek SY, Wurstbauer U, Wu SM, Glavin NR, Das S, Dash SP, Redwing JM, Robinson JA, Terrones M. Graphene and Beyond: Recent Advances in Two-Dimensional Materials Synthesis, Properties, and Devices. ACS NANOSCIENCE AU 2022; 2:450-485. [PMID: 36573124 PMCID: PMC9782807 DOI: 10.1021/acsnanoscienceau.2c00017] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/22/2022] [Accepted: 08/23/2022] [Indexed: 12/30/2022]
Abstract
Since the isolation of graphene in 2004, two-dimensional (2D) materials research has rapidly evolved into an entire subdiscipline in the physical sciences with a wide range of emergent applications. The unique 2D structure offers an open canvas to tailor and functionalize 2D materials through layer number, defects, morphology, moiré pattern, strain, and other control knobs. Through this review, we aim to highlight the most recent discoveries in the following topics: theory-guided synthesis for enhanced control of 2D morphologies, quality, yield, as well as insights toward novel 2D materials; defect engineering to control and understand the role of various defects, including in situ and ex situ methods; and properties and applications that are related to moiré engineering, strain engineering, and artificial intelligence. Finally, we also provide our perspective on the challenges and opportunities in this fascinating field.
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Affiliation(s)
- Yu Lei
- Department
of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Institute
of Materials Research, Tsinghua Shenzhen
International Graduate School, Shenzhen, Guangdong 518055, China,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Tianyi Zhang
- Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Material Science and Engineering, The
Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Yu-Chuan Lin
- Center
for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Material Science and Engineering, The
Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Tomotaroh Granzier-Nakajima
- Department
of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - George Bepete
- Department
of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Dorota A. Kowalczyk
- Department
of Solid State Physics, Faculty of Physics and Applied Informatics, University of Lodz, Pomorska 149/153, Lodz 90-236, Poland
| | - Zhong Lin
- Department
of Physics, University of Washington, Seattle, Washington 98195, United States
| | - Da Zhou
- Department
of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Thomas F. Schranghamer
- Department
of Engineering Science and Mechanics, Pennsylvania
State University, University Park, Pennsylvania 16802, United States
| | - Akhil Dodda
- Department
of Engineering Science and Mechanics, Pennsylvania
State University, University Park, Pennsylvania 16802, United States
| | - Amritanand Sebastian
- Department
of Engineering Science and Mechanics, Pennsylvania
State University, University Park, Pennsylvania 16802, United States
| | - Yifeng Chen
- Department
of Materials Science and Engineering, National
University of Singapore, 9 Engineering Drive, Singapore 117456, Singapore
| | - Yuanyue Liu
- Texas
Materials Institute and Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | | | - Thomas J. Kempa
- Department
of Chemistry, Johns Hopkins University, Baltimore, Maryland 21287, United States
| | - Bruno Schuler
- nanotech@surfaces
Laboratory, Empa − Swiss Federal
Laboratories for Materials Science and Technology, Dübendorf 8600, Switzerland
| | - Mark T. Edmonds
- School
of Physics and Astronomy, Monash University, Clayton, Victoria 3800, Australia
| | - Su Ying Quek
- Department
of Materials Science and Engineering, National
University of Singapore, 9 Engineering Drive, Singapore 117456, Singapore
| | - Ursula Wurstbauer
- Institute
of Physics, University of Münster, Wilhelm-Klemm-Str. 10, Münster 48149, Germany
| | - Stephen M. Wu
- Department
of Electrical and Computer Engineering & Department of Physics
and Astronomy, University of Rochester, Rochester, New York 14627, United States
| | - Nicholas R. Glavin
- Air
Force
Research Laboratory, Materials and Manufacturing Directorate, Wright-Patterson AFB, Dayton, Ohio 45433, United States
| | - Saptarshi Das
- Center
for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Material Science and Engineering, The
Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Engineering Science and Mechanics, Pennsylvania
State University, University Park, Pennsylvania 16802, United States
| | - Saroj Prasad Dash
- Department
of Microtechnology and Nanoscience, Chalmers
University of Technology, Göteborg SE-412 96, Sweden
| | - Joan M. Redwing
- Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Material Science and Engineering, The
Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Joshua A. Robinson
- Center
for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Material Science and Engineering, The
Pennsylvania State University, University Park, Pennsylvania 16802, United States,
| | - Mauricio Terrones
- Department
of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Material Science and Engineering, The
Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Research
Initiative for Supra-Materials and Global Aqua Innovation Center, Shinshu University, 4-17-1Wakasato, Nagano 380-8553, Japan,
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19
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Waltl M, Knobloch T, Tselios K, Filipovic L, Stampfer B, Hernandez Y, Waldhör D, Illarionov Y, Kaczer B, Grasser T. Perspective of 2D Integrated Electronic Circuits: Scientific Pipe Dream or Disruptive Technology? ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2201082. [PMID: 35318749 DOI: 10.1002/adma.202201082] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/14/2022] [Indexed: 06/14/2023]
Abstract
Within the last decade, considerable efforts have been devoted to fabricating transistors utilizing 2D semiconductors. Also, small circuits consisting of a few transistors have been demonstrated, including inverters, ring oscillators, and static random access memory cells. However, for industrial applications, both time-zero and time-dependent variability in the performance of the transistors appear critical. While time-zero variability is primarily related to immature processing, time-dependent drifts are dominated by charge trapping at defects located at the channel/insulator interface and in the insulator itself, which can substantially degrade the stability of circuits. At the current state of the art, 2D transistors typically exhibit a few orders of magnitude higher trap densities than silicon devices, which considerably increases their time-dependent variability, resulting in stability and yield issues. Here, the stability of currently available 2D electronics is carefully evaluated using circuit simulations to determine the impact of transistor-related issues on the overall circuit performance. The results suggest that while the performance parameters of transistors based on certain material combinations are already getting close to being competitive with Si technologies, a reduction in variability and defect densities is required. Overall, the criteria for parameter variability serve as guidance for evaluating the future development of 2D technologies.
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Affiliation(s)
- Michael Waltl
- Christian Doppler Laboratory for Single-Defect Spectroscopy at the Institute for Microelectronics, TU Wien, Gusshausstrasse 27-29, Vienna, 1040, Austria
| | - Theresia Knobloch
- Institute for Microelectronics, TU Wien, Gusshausstrasse 27-29, Vienna, 1040, Austria
| | - Konstantinos Tselios
- Christian Doppler Laboratory for Single-Defect Spectroscopy at the Institute for Microelectronics, TU Wien, Gusshausstrasse 27-29, Vienna, 1040, Austria
| | - Lado Filipovic
- Institute for Microelectronics, TU Wien, Gusshausstrasse 27-29, Vienna, 1040, Austria
| | - Bernhard Stampfer
- Christian Doppler Laboratory for Single-Defect Spectroscopy at the Institute for Microelectronics, TU Wien, Gusshausstrasse 27-29, Vienna, 1040, Austria
| | - Yoanlys Hernandez
- Institute for Microelectronics, TU Wien, Gusshausstrasse 27-29, Vienna, 1040, Austria
| | - Dominic Waldhör
- Institute for Microelectronics, TU Wien, Gusshausstrasse 27-29, Vienna, 1040, Austria
| | - Yury Illarionov
- Institute for Microelectronics, TU Wien, Gusshausstrasse 27-29, Vienna, 1040, Austria
- Ioffe Institute, Polytechnicheskaya 26, St-Petersburg, 194021, Russia
| | - Ben Kaczer
- imec, Kapeldreef 75, Leuven, 3001, Belgium
| | - Tibor Grasser
- Institute for Microelectronics, TU Wien, Gusshausstrasse 27-29, Vienna, 1040, Austria
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20
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Wang S, Liu X, Zhou P. The Road for 2D Semiconductors in the Silicon Age. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2106886. [PMID: 34741478 DOI: 10.1002/adma.202106886] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/21/2021] [Indexed: 06/13/2023]
Abstract
Continued reduction in transistor size can improve the performance of silicon integrated circuits (ICs). However, as Moore's law approaches physical limits, high-performance growth in silicon ICs becomes unsustainable, due to challenges of scaling, energy efficiency, and memory limitations. The ultrathin layers, diverse band structures, unique electronic properties, and silicon-compatible processes of 2D materials create the potential to consistently drive advanced performance in ICs. Here, the potential of fusing 2D materials with silicon ICs to minimize the challenges in silicon ICs, and to create technologies beyond the von Neumann architecture, is presented, and the killer applications for 2D materials in logic and memory devices to ease scaling, energy efficiency bottlenecks, and memory dilemmas encountered in silicon ICs are discussed. The fusion of 2D materials allows the creation of all-in-one perception, memory, and computation technologies beyond the von Neumann architecture to enhance system efficiency and remove computing power bottlenecks. Progress on the 2D ICs demonstration is summarized, as well as the technical hurdles it faces in terms of wafer-scale heterostructure growth, transfer, and compatible integration with silicon ICs. Finally, the promising pathways and obstacles to the technological advances in ICs due to the integration of 2D materials with silicon are presented.
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Affiliation(s)
- Shuiyuan Wang
- ASIC & System State Key Lab, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Xiaoxian Liu
- ASIC & System State Key Lab, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Peng Zhou
- ASIC & System State Key Lab, School of Microelectronics, Fudan University, Shanghai, 200433, China
- Frontier Institute of Chip and System, Shanghai Frontier Base of Intelligent Optoelectronics and Perception, Institute of Optoelectronics, Fudan University, Shanghai, 200433, China
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21
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Sebastian A, Pendurthi R, Kozhakhmetov A, Trainor N, Robinson JA, Redwing JM, Das S. Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks. Nat Commun 2022; 13:6139. [PMID: 36253370 PMCID: PMC9576759 DOI: 10.1038/s41467-022-33699-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 09/27/2022] [Indexed: 12/24/2022] Open
Abstract
Artificial neural networks have demonstrated superiority over traditional computing architectures in tasks such as pattern classification and learning. However, they do not measure uncertainty in predictions, and hence they can make wrong predictions with high confidence, which can be detrimental for many mission-critical applications. In contrast, Bayesian neural networks (BNNs) naturally include such uncertainty in their model, as the weights are represented by probability distributions (e.g. Gaussian distribution). Here we introduce three-terminal memtransistors based on two-dimensional (2D) materials, which can emulate both probabilistic synapses as well as reconfigurable neurons. The cycle-to-cycle variation in the programming of the 2D memtransistor is exploited to achieve Gaussian random number generator-based synapses, whereas 2D memtransistor based integrated circuits are used to obtain neurons with hyperbolic tangent and sigmoid activation functions. Finally, memtransistor-based synapses and neurons are combined in a crossbar array architecture to realize a BNN accelerator for a data classification task.
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Affiliation(s)
- Amritanand Sebastian
- grid.29857.310000 0001 2097 4281Deparment of Engineering Science and Mechanics, Penn State University, University Park, PA 16802 USA
| | - Rahul Pendurthi
- grid.29857.310000 0001 2097 4281Deparment of Engineering Science and Mechanics, Penn State University, University Park, PA 16802 USA
| | - Azimkhan Kozhakhmetov
- grid.29857.310000 0001 2097 4281Department of Materials Science and Engineering, Penn State University, University Park, PA 16802 USA
| | - Nicholas Trainor
- grid.29857.310000 0001 2097 4281Department of Materials Science and Engineering, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 42812D Crystal Consortium Materials Innovation Platform, Penn State University, University Park, PA 16802 USA
| | - Joshua A. Robinson
- grid.29857.310000 0001 2097 4281Department of Materials Science and Engineering, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 4281Department of Chemistry, Penn State University, University Park, PA USA ,grid.29857.310000 0001 2097 4281Department of Physics, Penn State University, University Park, PA USA
| | - Joan M. Redwing
- grid.29857.310000 0001 2097 4281Department of Materials Science and Engineering, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 42812D Crystal Consortium Materials Innovation Platform, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 4281Department of Electrical Engineering and Computer Science, Penn State University, University Park, PA USA
| | - Saptarshi Das
- grid.29857.310000 0001 2097 4281Deparment of Engineering Science and Mechanics, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 4281Department of Materials Science and Engineering, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 4281Department of Electrical Engineering and Computer Science, Penn State University, University Park, PA USA
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22
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Das S, Das S. Digital Keying Enabled by Reconfigurable 2D Modulators. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2203753. [PMID: 36057140 DOI: 10.1002/adma.202203753] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 08/21/2022] [Indexed: 06/15/2023]
Abstract
Energy, area, and bandwidth efficient communication primitives are essential to sustain the rapid increase in connectivity among internet-of-things (IoT) edge devices. While IoT edge-sensing, edge-computing, and edge-storage have witnessed innovation in materials and devices, IoT edge communication is yet to experience such transformation. The aging silicon (Si)-based complementary metal-oxide-semiconductor (CMOS) technology continues to remain the mainstay of communication devices where they are used to implement amplitude, frequency, and phase shift keying (amplitude-shift keying [ASK]/frequency-shift keying [FSK]/phase-shift keying [PSK]). Keying allows digital information to be communicated over a radio channel. While CMOS-based keying devices have evolved over the years, their hardware footprint and energy consumption are major concerns for resource constrained IoT communication. Furthermore, separate circuit designs and hardware elements are needed for each keying scheme and achieving multibit modulation to improve bandwidth efficiency remains a challenge. Here, a reconfigurable modulator is introduced that exploits unique ambipolar transport and programmable Dirac voltage in ultrathin MoTe2 field-effect transistors to achieve ASK, FSK, and PSK modulation. Furthermore, by integrating two programmed MoTe2 field-effect transistors, multibit data modulation is demonstrated, which improves the bandwidth efficiency by 200%. Finally, a frequency quadrupler is also realized exploiting the unique "double-well" transfer characteristic.
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Affiliation(s)
- Sarbashis Das
- Electrical Engineering, Pennsylvania State University, University Park, PA, 16802, USA
| | - Saptarshi Das
- Electrical Engineering, Pennsylvania State University, University Park, PA, 16802, USA
- Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16802, USA
- Material Research Institute, Pennsylvania State University, University Park, PA, 16802, USA
- Materials Science and Engineering, Pennsylvania State University, University Park, PA, 16802, USA
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23
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Zheng Y, Ravichandran H, Schranghamer TF, Trainor N, Redwing JM, Das S. Hardware implementation of Bayesian network based on two-dimensional memtransistors. Nat Commun 2022; 13:5578. [PMID: 36151079 PMCID: PMC9508127 DOI: 10.1038/s41467-022-33053-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 08/31/2022] [Indexed: 11/30/2022] Open
Abstract
Bayesian networks (BNs) find widespread application in many real-world probabilistic problems including diagnostics, forecasting, computer vision, etc. The basic computing primitive for BNs is a stochastic bit (s-bit) generator that can control the probability of obtaining ‘1’ in a binary bit-stream. While silicon-based complementary metal-oxide-semiconductor (CMOS) technology can be used for hardware implementation of BNs, the lack of inherent stochasticity makes it area and energy inefficient. On the other hand, memristors and spintronic devices offer inherent stochasticity but lack computing ability beyond simple vector matrix multiplication due to their two-terminal nature and rely on extensive CMOS peripherals for BN implementation, which limits area and energy efficiency. Here, we circumvent these challenges by introducing a hardware platform based on 2D memtransistors. First, we experimentally demonstrate a low-power and compact s-bit generator circuit that exploits cycle-to-cycle fluctuation in the post-programmed conductance state of 2D memtransistors. Next, the s-bit generators are monolithically integrated with 2D memtransistor-based logic gates to implement BNs. Our findings highlight the potential for 2D memtransistor-based integrated circuits for non-von Neumann computing applications. Bayesian networks are applied to resolve several types of probabilistic problems. Here, Das et al. develop a stochastic computing hardware platform using two-dimensional memtransistors for the implementation of Bayesian network with high accuracy.
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Affiliation(s)
- Yikai Zheng
- Engineering Science and Mechanics, Penn State University, University Park, 16802, PA, USA
| | | | - Thomas F Schranghamer
- Engineering Science and Mechanics, Penn State University, University Park, 16802, PA, USA
| | - Nicholas Trainor
- Materials Science and Engineering, Penn State University, University Park, 16802, PA, USA.,Materials Research Institute, Penn State University, University Park, 16802, PA, USA
| | - Joan M Redwing
- Materials Science and Engineering, Penn State University, University Park, 16802, PA, USA.,Materials Research Institute, Penn State University, University Park, 16802, PA, USA
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, 16802, PA, USA. .,Materials Science and Engineering, Penn State University, University Park, 16802, PA, USA. .,Materials Research Institute, Penn State University, University Park, 16802, PA, USA. .,Electrical Engineering and Computer Science, Penn State University, University Park, 16802, PA, USA.
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24
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Pendurthi R, Jayachandran D, Kozhakhmetov A, Trainor N, Robinson JA, Redwing JM, Das S. Heterogeneous Integration of Atomically Thin Semiconductors for Non-von Neumann CMOS. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2202590. [PMID: 35843869 DOI: 10.1002/smll.202202590] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 06/21/2022] [Indexed: 06/15/2023]
Abstract
Atomically thin, 2D, and semiconducting transition metal dichalcogenides (TMDs) are seen as potential candidates for complementary metal oxide semiconductor (CMOS) technology in future nodes. While high-performance field effect transistors (FETs), logic gates, and integrated circuits (ICs) made from n-type TMDs such as MoS2 and WS2 grown at wafer scale have been demonstrated, realizing CMOS electronics necessitates integration of large area p-type semiconductors. Furthermore, the physical separation of memory and logic is a bottleneck of the existing CMOS technology and must be overcome to reduce the energy burden for computation. In this article, the existing limitations are overcome and for the first time, a heterogeneous integration of large area grown n-type MoS2 and p-type vanadium doped WSe2 FETs with non-volatile and analog memory storage capabilities to achieve a non-von Neumann 2D CMOS platform is introduced. This manufacturing process flow allows for precise positioning of n-type and p-type FETs, which is critical for any IC development. Inverters and a simplified 2-input-1-output multiplexers and neuromorphic computing primitives such as Gaussian, sigmoid, and tanh activation functions using this non-von Neumann 2D CMOS platform are also demonstrated. This demonstration shows the feasibility of heterogeneous integration of wafer scale 2D materials.
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Affiliation(s)
- Rahul Pendurthi
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Darsith Jayachandran
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Azimkhan Kozhakhmetov
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
| | - Nicholas Trainor
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- 2D Crystal Consortium - Materials Innovation Platform (2DCC-MIP) Materials Research Institute, Penn State University, University Park, PA, 16802, USA
| | - Joshua A Robinson
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- 2D Crystal Consortium - Materials Innovation Platform (2DCC-MIP) Materials Research Institute, Penn State University, University Park, PA, 16802, USA
| | - Joan M Redwing
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- 2D Crystal Consortium - Materials Innovation Platform (2DCC-MIP) Materials Research Institute, Penn State University, University Park, PA, 16802, USA
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- 2D Crystal Consortium - Materials Innovation Platform (2DCC-MIP) Materials Research Institute, Penn State University, University Park, PA, 16802, USA
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, 16802, USA
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25
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Yu LY, Ren GP, Hou XJ, Wu KJ, He Y. Transition State Theory-Inspired Neural Network for Estimating the Viscosity of Deep Eutectic Solvents. ACS CENTRAL SCIENCE 2022; 8:983-995. [PMID: 35912349 PMCID: PMC9335917 DOI: 10.1021/acscentsci.2c00157] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Indexed: 06/15/2023]
Abstract
The lack of accurate methods for predicting the viscosity of solvent materials, especially those with complex interactions, remains unresolved. Deep eutectic solvents (DESs), an emerging class of green solvents, have a severe lack of viscosity data, resulting in their application still staying at the stage of random trial and error, and it is difficult for them to be implemented on an industrial scale. In this work, we demonstrate the successful prediction of the viscosity of DESs based on the transition state theory-inspired neural network (TSTiNet). The TSTiNet adopts multilayer perceptron (MLP) for the transition state theory-inspired equation (TSTiEq) parameters calculation and verification using the most comprehensive DESs viscosity data set to date. For the energy parameters of the TSTiEq, the constant assumption and the fast iteration with the help of MLP can allow TSTiNet to achieve the best performance (the average absolute relative deviation on the test set of 6.84% and R 2 of 0.9805). Compared with the traditional machine learning methods, the TSTiNet has better generalization ability and dramatically reduces the maximum relative deviation of prediction under the constraints of the thermodynamic formulation. It requires only the structural information on DESs and is the most accurate and reliable model available for DESs viscosity prediction.
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Affiliation(s)
- Liu-Ying Yu
- Zhejiang
Provincial Key Laboratory of Advanced Chemical Engineering Manufacture
Technology, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
- Institute
of Zhejiang University-Quzhou, Quzhou 324000, China
| | - Gao-Peng Ren
- Zhejiang
Provincial Key Laboratory of Advanced Chemical Engineering Manufacture
Technology, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
| | - Xiao-Jing Hou
- Zhejiang
Provincial Key Laboratory of Advanced Chemical Engineering Manufacture
Technology, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
- Institute
of Zhejiang University-Quzhou, Quzhou 324000, China
| | - Ke-Jun Wu
- Zhejiang
Provincial Key Laboratory of Advanced Chemical Engineering Manufacture
Technology, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
- Institute
of Zhejiang University-Quzhou, Quzhou 324000, China
- School
of Chemical and Process Engineering, University
of Leeds, Leeds LS2 9JT, U.K.
| | - Yuchen He
- State
Key Laboratory of Industrial Control Technology, College of Control
Science and Engineering, Zhejiang University, Hangzhou 310027, China
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26
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All-in-one, bio-inspired, and low-power crypto engines for near-sensor security based on two-dimensional memtransistors. Nat Commun 2022; 13:3587. [PMID: 35739100 PMCID: PMC9226122 DOI: 10.1038/s41467-022-31148-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/31/2022] [Indexed: 11/15/2022] Open
Abstract
In the emerging era of the internet of things (IoT), ubiquitous sensors continuously collect, consume, store, and communicate a huge volume of information which is becoming increasingly vulnerable to theft and misuse. Modern software cryptosystems require extensive computational infrastructure for implementing ciphering algorithms, making them difficult to be adopted by IoT edge sensors that operate with limited hardware resources and at low energy budgets. Here we propose and experimentally demonstrate an “all-in-one” 8 × 8 array of robust, low-power, and bio-inspired crypto engines monolithically integrated with IoT edge sensors based on two-dimensional (2D) memtransistors. Each engine comprises five 2D memtransistors to accomplish sensing and encoding functionalities. The ciphered information is shown to be secure from an eavesdropper with finite resources and access to deep neural networks. Our hardware platform consists of a total of 320 fully integrated monolayer MoS2-based memtransistors and consumes energy in the range of hundreds of picojoules and offers near-sensor security. Internet of things (IoT) sensors can collect, store and communicate large volumes of information, which require effective security measures. Here, the authors report the realization of low-power edge sensors based on photosensitive and programmable 2D memtransistors, integrating sensing, storage and encryption functionalities.
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27
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Dai C, Liu Y, Wei D. Two-Dimensional Field-Effect Transistor Sensors: The Road toward Commercialization. Chem Rev 2022; 122:10319-10392. [PMID: 35412802 DOI: 10.1021/acs.chemrev.1c00924] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The evolutionary success in information technology has been sustained by the rapid growth of sensor technology. Recently, advances in sensor technology have promoted the ambitious requirement to build intelligent systems that can be controlled by external stimuli along with independent operation, adaptivity, and low energy expenditure. Among various sensing techniques, field-effect transistors (FETs) with channels made of two-dimensional (2D) materials attract increasing attention for advantages such as label-free detection, fast response, easy operation, and capability of integration. With atomic thickness, 2D materials restrict the carrier flow within the material surface and expose it directly to the external environment, leading to efficient signal acquisition and conversion. This review summarizes the latest advances of 2D-materials-based FET (2D FET) sensors in a comprehensive manner that contains the material, operating principles, fabrication technologies, proof-of-concept applications, and prototypes. First, a brief description of the background and fundamentals is provided. The subsequent contents summarize physical, chemical, and biological 2D FET sensors and their applications. Then, we highlight the challenges of their commercialization and discuss corresponding solution techniques. The following section presents a systematic survey of recent progress in developing commercial prototypes. Lastly, we summarize the long-standing efforts and prospective future development of 2D FET-based sensing systems toward commercialization.
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Affiliation(s)
- Changhao Dai
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China.,Laboratory of Molecular Materials and Devices, Fudan University, Shanghai 200433, China
| | - Yunqi Liu
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai 200433, China
| | - Dacheng Wei
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China.,Laboratory of Molecular Materials and Devices, Fudan University, Shanghai 200433, China
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28
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Lin J, Liu H, Wang S, Wang D, Wu L. The Image Identification Application with HfO 2-Based Replaceable 1T1R Neural Networks. NANOMATERIALS 2022; 12:nano12071075. [PMID: 35407193 PMCID: PMC9000711 DOI: 10.3390/nano12071075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/11/2022] [Accepted: 03/23/2022] [Indexed: 11/16/2022]
Abstract
This paper mainly studies the hardware implementation of a fully connected neural network based on the 1T1R (one-transistor-one-resistor) array and its application in handwritten digital image recognition. The 1T1R arrays are prepared by connecting the memristor and nMOSFET in series, and a single-layer and a double-layer fully connected neural network are established. The recognition accuracy of 8 × 8 handwritten digital images reaches 95.19%. By randomly replacing the devices with failed devices, it is found that the stuck-off devices have little effect on the accuracy of the network, but the stuck-on devices will cause a sharp reduction of accuracy. By using the measured conductivity adjustment range and precision data of the memristor, the relationship between the recognition accuracy of the network and the number of hidden neurons is simulated. The simulation results match the experimental results. Compared with the neural network based on the precision of 32-bit floating point, the difference is lower than 1%.
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29
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Sebastian A, Das S, Das S. An Annealing Accelerator for Ising Spin Systems Based on In-Memory Complementary 2D FETs. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2107076. [PMID: 34761447 DOI: 10.1002/adma.202107076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/20/2021] [Indexed: 06/13/2023]
Abstract
Metaheuristic algorithms such as simulated annealing (SA) are often implemented for optimization in combinatorial problems, especially for discreet problems. SA employs a stochastic search, where high-energy transitions ("hill-climbing") are allowed with a temperature-dependent probability to escape local optima. Ising spin glass systems have properties such as spin disorder and "frustration" and provide a discreet combinatorial problem with a high number of metastable states and ground-state degeneracy. In this work, subthreshold Boltzmann transport is exploited in complementary 2D field-effect transistors (p-type WSe2 and n-type MoS2 ) integrated with an analog, nonvolatile, and programmable floating-gate memory stack to develop in-memory computing primitives necessary for energy- and area-efficient hardware acceleration of SA for Ising spin systems. Search acceleration of >800× is demonstrated for 4 × 4 ferromagnetic, antiferromagnetic, and spin glass systems using SA compared to an exhaustive search using a brute force trial at miniscule total energy expenditure of ≈120 nJ. The hardware-realistic numerical simulations further highlight the astounding benefits of SA in accelerating the search for larger spin lattices.
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Affiliation(s)
- Amritanand Sebastian
- Deparment of Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Sarbashis Das
- Department of Electrical Engineering, Penn State University, University Park, PA, 16802, USA
| | - Saptarshi Das
- Deparment of Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
- Department of Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- Materials Research Institute, Pennsylvania State University, University Park, PA, 16802, USA
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30
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Oberoi A, Dodda A, Liu H, Terrones M, Das S. Secure Electronics Enabled by Atomically Thin and Photosensitive Two-Dimensional Memtransistors. ACS NANO 2021; 15:19815-19827. [PMID: 34914350 DOI: 10.1021/acsnano.1c07292] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
The rapid proliferation of security compromised hardware in today's integrated circuit (IC) supply chain poses a global threat to the reliability of communication, computing, and control systems. While there have been significant advancements in detection and avoidance of security breaches, current top-down approaches are mostly inadequate, inefficient, often inconclusive, and resource extensive in time, energy, and cost, offering tremendous scope for innovation in this field. Here, we introduce an energy and area efficient non-von Neumann hardware platform providing comprehensive and bottom-up security solutions by exploiting inherent device-to-device variation, electrical programmability, and persistent photoconductivity demonstrated by atomically thin two-dimensional memtransistors. We realize diverse security primitives including physically unclonable function, anticounterfeit measures, intellectual property (IP) watermarking, and IC camouflaging to prevent false authentication, detect recycled and remarked ICs, protect IP theft, and stop reverse engineering of ICs.
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Affiliation(s)
- Aaryan Oberoi
- Deparment of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Akhil Dodda
- Deparment of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - He Liu
- Department of Chemistry, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Mauricio Terrones
- Department of Chemistry, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Physics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Saptarshi Das
- Deparment of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania 16802, United States
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31
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Thakar K, Lodha S. Multi-Bit Analog Transmission Enabled by Electrostatically Reconfigurable Ambipolar and Anti-Ambipolar Transport. ACS NANO 2021; 15:19692-19701. [PMID: 34890505 DOI: 10.1021/acsnano.1c07032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Various analog applications, such as phase switching, have been demonstrated using either ambipolar or anti-ambipolar transport in two-dimensional materials. However, the availability of only one transport mode severely limits the application scope and range. This work demonstrates electrostatically reconfigurable and tunable ambipolar and anti-ambipolar transport in the same field-effect transistor using a photoactive ambipolar WSe2 channel with gate-controlled channel and Schottky barriers. This enables the realization of in-phase, out-of-phase, and double-frequency sinusoidal output signals under dark and illumination conditions. The output waveforms were used to generate phase-, frequency-, and amplitude-modulated analog schemes for 2- and 3-bit data transmission. Evaluation of all possible schemes for their power consumption, error probability, and implementation complexity highlights the importance of switching between ambipolar and anti-ambipolar modes of transport for best transmission performance. A dual-metal contact transistor with improved linearity for harmonic and excess power suppression demonstrates further performance enhancement. Generic device architecture and operation makes this work adaptable to any ambipolar material amenable to electrostatic control.
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Affiliation(s)
- Kartikey Thakar
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Saurabh Lodha
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
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32
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Bagheriye L, Kwisthout J. Brain-Inspired Hardware Solutions for Inference in Bayesian Networks. Front Neurosci 2021; 15:728086. [PMID: 34924925 PMCID: PMC8677599 DOI: 10.3389/fnins.2021.728086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 10/11/2021] [Indexed: 11/23/2022] Open
Abstract
The implementation of inference (i.e., computing posterior probabilities) in Bayesian networks using a conventional computing paradigm turns out to be inefficient in terms of energy, time, and space, due to the substantial resources required by floating-point operations. A departure from conventional computing systems to make use of the high parallelism of Bayesian inference has attracted recent attention, particularly in the hardware implementation of Bayesian networks. These efforts lead to several implementations ranging from digital circuits, mixed-signal circuits, to analog circuits by leveraging new emerging nonvolatile devices. Several stochastic computing architectures using Bayesian stochastic variables have been proposed, from FPGA-like architectures to brain-inspired architectures such as crossbar arrays. This comprehensive review paper discusses different hardware implementations of Bayesian networks considering different devices, circuits, and architectures, as well as a more futuristic overview to solve existing hardware implementation problems.
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Affiliation(s)
- Leila Bagheriye
- Foundations of Natural and Stochastic Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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33
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Dodda A, Das S. Demonstration of Stochastic Resonance, Population Coding, and Population Voting Using Artificial MoS 2 Based Synapses. ACS NANO 2021; 15:16172-16182. [PMID: 34648278 DOI: 10.1021/acsnano.1c05042] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Fast detection of weak signals at low energy expenditure is a challenging but inescapable task for the evolutionary success of animals that survive in resource constrained environments. This task is accomplished by the sensory nervous system by exploiting the synergy between three astounding neural phenomena, namely, stochastic resonance (SR), population coding (PC), and population voting (PV). In SR, the constructive role of synaptic noise is exploited for the detection of otherwise invisible signals. In PC, the redundancy in neural population is exploited to reduce the detection latency. Finally, PV ensures unambiguous signal detection even in the presence of excessive noise. Here we adopt a similar strategies and experimentally demonstrate how a population of stochastic artificial neurons based on monolayer MoS2 field effect transistors (FETs) can use an optimum amount of white Gaussian noise and population voting to detect invisible signals at a frugal energy expenditure (∼10s of nano-Joules). Our findings can aid remote sensing in the emerging era of the Internet of things (IoT) that thrive on energy efficiency.
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Affiliation(s)
- Akhil Dodda
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Saptarshi Das
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania 16802, United States
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34
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Macewicz L, Pyrchla K, Bogdanowicz R, Sumanasekera G, Jasinski JB. Chemical Vapor Transport Route toward Black Phosphorus Nanobelts and Nanoribbons. J Phys Chem Lett 2021; 12:8347-8354. [PMID: 34432469 DOI: 10.1021/acs.jpclett.1c02064] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Chemical vapor transport (CVT) method is widely used for bulk black phosphorus (BP) fabrication. In this work, we demonstrate that CVT provides a route for the fabrication of BP nanoribbons and nanobelts. This method consists of a two-step procedure, including initial BP column growth using the CVT technique, followed by ultrasonic treatment and centrifugation. The obtained nanostructures preserve BP column dimensions, forming ultralong ribbon-like structures with the length to the width aspect ratio of up to 500. Computational modeling of the growth mechanism of a BP flake is also presented in support of the observed columnar growth. Calculation of the average energy of the molecule in the asymmetric flakes shows that the growth of the structure in the zigzag direction is more energetically favorable than in the armchair direction.
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Affiliation(s)
- Lukasz Macewicz
- Telecommunications and Informatics, Faculty of Electronics, Gdańsk University of Technology, Narutowicza Street 11/12, 80-233 Gdańsk, Poland
- Conn Center for Renewable Energy Research, University of Louisville, Louisville, Kentucky 40292, United States
| | - Krzysztof Pyrchla
- Telecommunications and Informatics, Faculty of Electronics, Gdańsk University of Technology, Narutowicza Street 11/12, 80-233 Gdańsk, Poland
| | - Robert Bogdanowicz
- Telecommunications and Informatics, Faculty of Electronics, Gdańsk University of Technology, Narutowicza Street 11/12, 80-233 Gdańsk, Poland
| | - Gamini Sumanasekera
- Department of Physics, University of Louisville, Louisville, Kentucky 40292, United States
| | - Jacek B Jasinski
- Conn Center for Renewable Energy Research, University of Louisville, Louisville, Kentucky 40292, United States
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35
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Schranghamer TF, Sharma M, Singh R, Das S. Review and comparison of layer transfer methods for two-dimensional materials for emerging applications. Chem Soc Rev 2021; 50:11032-11054. [PMID: 34397050 DOI: 10.1039/d1cs00706h] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Two-dimensional (2D) materials offer immense potential for scientific breakthroughs and technological innovations. While early demonstrations of 2D material-based electronics, optoelectronics, flextronics, straintronics, twistronics, and biomimetic devices exploited micromechanically-exfoliated single crystal flakes, recent years have witnessed steady progress in large-area growth techniques such as physical vapor deposition (PVD), chemical vapor deposition (CVD), and metal-organic CVD (MOCVD). However, use of high growth temperatures, chemically-active growth precursors and promoters, and the need for epitaxy often limit direct growth of 2D materials on the substrates of interest for commercial applications. This has led to the development of a large number of methods for the layer transfer of 2D materials from the growth substrate to the target application substrate with varying degrees of cleanliness, uniformity, and transfer-related damage. This review aims to catalog and discuss these layer transfer methods. In particular, the processes, advantages, and drawbacks of various transfer methods are discussed, as is their applicability to different technological platforms of interest for 2D material implementation.
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Affiliation(s)
- Thomas F Schranghamer
- Department of Engineering Science and Mechanics, Penn State University, University Park, PA 16802, USA.
| | - Madan Sharma
- Department of Physics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Rajendra Singh
- Department of Physics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Saptarshi Das
- Department of Engineering Science and Mechanics, Penn State University, University Park, PA 16802, USA. and Department of Materials Science and Engineering, Penn State University, University Park, PA 16802, USA and Materials Research Institute, Penn State University, University Park, PA 16802, USA
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36
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Yin L, Cheng R, Wen Y, Liu C, He J. Emerging 2D Memory Devices for In-Memory Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2007081. [PMID: 34105195 DOI: 10.1002/adma.202007081] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 12/27/2020] [Indexed: 06/12/2023]
Abstract
It is predicted that the conventional von Neumann computing architecture cannot meet the demands of future data-intensive computing applications due to the bottleneck between the processing and memory units. To try to solve this problem, in-memory computing technology, where calculations are carried out in situ within each nonvolatile memory unit, has been intensively studied. Among various candidate materials, 2D layered materials have recently demonstrated many new features that have been uniquely exploited to build next-generation electronics. Here, the recent progress of 2D memory devices is reviewed for in-memory computing. For each memory configuration, their operation mechanisms and memory characteristics are described, and their pros and cons are weighed. Subsequently, their versatile applications for in-memory computing technology, including logic operations, electronic synapses, and random number generation are presented. Finally, the current challenges and potential strategies for future 2D in-memory computing systems are also discussed at the material, device, circuit, and architecture levels. It is hoped that this manuscript could give a comprehensive review of 2D memory devices and their applications in in-memory computing, and be helpful for this exciting research area.
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Affiliation(s)
- Lei Yin
- Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, and School of Physics and Technology, Wuhan University, Wuhan, 430072, P. R. China
| | - Ruiqing Cheng
- Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, and School of Physics and Technology, Wuhan University, Wuhan, 430072, P. R. China
| | - Yao Wen
- Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, and School of Physics and Technology, Wuhan University, Wuhan, 430072, P. R. China
| | - Chuansheng Liu
- Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, and School of Physics and Technology, Wuhan University, Wuhan, 430072, P. R. China
| | - Jun He
- Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, and School of Physics and Technology, Wuhan University, Wuhan, 430072, P. R. China
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37
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Subbulakshmi Radhakrishnan S, Sebastian A, Oberoi A, Das S, Das S. A biomimetic neural encoder for spiking neural network. Nat Commun 2021; 12:2143. [PMID: 33837210 PMCID: PMC8035177 DOI: 10.1038/s41467-021-22332-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 03/09/2021] [Indexed: 02/07/2023] Open
Abstract
Spiking neural networks (SNNs) promise to bridge the gap between artificial neural networks (ANNs) and biological neural networks (BNNs) by exploiting biologically plausible neurons that offer faster inference, lower energy expenditure, and event-driven information processing capabilities. However, implementation of SNNs in future neuromorphic hardware requires hardware encoders analogous to the sensory neurons, which convert external/internal stimulus into spike trains based on specific neural algorithm along with inherent stochasticity. Unfortunately, conventional solid-state transducers are inadequate for this purpose necessitating the development of neural encoders to serve the growing need of neuromorphic computing. Here, we demonstrate a biomimetic device based on a dual gated MoS2 field effect transistor (FET) capable of encoding analog signals into stochastic spike trains following various neural encoding algorithms such as rate-based encoding, spike timing-based encoding, and spike count-based encoding. Two important aspects of neural encoding, namely, dynamic range and encoding precision are also captured in our demonstration. Furthermore, the encoding energy was found to be as frugal as ≈1-5 pJ/spike. Finally, we show fast (≈200 timesteps) encoding of the MNIST data set using our biomimetic device followed by more than 91% accurate inference using a trained SNN.
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Affiliation(s)
| | - Amritanand Sebastian
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA
| | - Aaryan Oberoi
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA
| | - Sarbashis Das
- Department of Electrical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Saptarshi Das
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA.
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, PA, USA.
- Materials Research Institute, Pennsylvania State University, University Park, PA, USA.
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38
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Yang Q, Yang H, Lv D, Yu R, Li E, He L, Chen Q, Chen H, Guo T. High-Performance Organic Synaptic Transistors with an Ultrathin Active Layer for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2021; 13:8672-8681. [PMID: 33565852 DOI: 10.1021/acsami.0c22271] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In recent years, much attention has been focused on two-dimensional (2D) material-based synaptic transistor devices because of their inherent advantages of low dimension, simultaneous read-write operation and high efficiency. However, process compatibility and repeatability of these materials are still a big challenge, as well as other issues such as complex transfer process and material selectivity. In this work, synaptic transistors with an ultrathin organic semiconductor layer (down to 7 nm) were obtained by the simple dip-coating process, which exhibited a high current switch ratio up to 106, well off state as low as nearly 10-12 A, and low operation voltage of -3 V. Moreover, various synaptic behaviors were successfully simulated including excitatory postsynaptic current, paired pulse facilitation, long-term potentiation, and long-term depression. More importantly, under ultrathin conditions, excellent memory preservation, and linearity of weight update were obtained because of the enhanced effect of defects and improved controllability of the gate voltage on the ultrathin active layer, which led to a pattern recognition rate up to 85%. This is the first work to demonstrate that the pattern recognition rate, a crucial parameter for neuromorphic computing can be significantly improved by reducing the thickness of the channel layer. Hence, these results not only reveal a simple and effective way to improve plasticity and memory retention of the artificial synapse via thickness modulation but also expand the material selection for the 2D artificial synaptic devices.
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Affiliation(s)
- Qian Yang
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Zhicheng College, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
| | - Huihuang Yang
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
| | - Dongxu Lv
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
| | - Rengjian Yu
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
| | - Enlong Li
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
| | - Lihua He
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
| | - Qizhen Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
| | - Huipeng Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
| | - Tailiang Guo
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
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39
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Wali A, Kundu S, Arnold AJ, Zhao G, Basu K, Das S. Satisfiability Attack-Resistant Camouflaged Two-Dimensional Heterostructure Devices. ACS NANO 2021; 15:3453-3467. [PMID: 33507060 DOI: 10.1021/acsnano.0c10651] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Reverse engineering (RE) is one of the major security threats to the semiconductor industry due to the involvement of untrustworthy parties in an increasingly globalized chip manufacturing supply chain. RE efforts have already been successful in extracting device level functionalities from an integrated circuit (IC) with very limited resources. Camouflaging is an obfuscation method that can thwart such RE. Existing work on IC camouflaging primarily involves transformable interconnects and/or covert gates where variation in doping and dummy contacts hide the circuit structure or build cells that look alike but have different functionalities. Emerging solutions, such as polymorphic gates based on a giant spin Hall effect and Si nanowire field effect transistors (FETs), are also promising but add significant area overhead and are successfully decamouflaged by the satisfiability solver (SAT)-based RE techniques. Here, we harness the properties of two-dimensional (2D) transition-metal dichalcogenides (TMDs) including MoS2, MoSe2, MoTe2, WS2, and WSe2 and their optically transparent transition-metal oxides (TMOs) to demonstrate area efficient camouflaging solutions that are resilient to SAT attack and automatic test pattern generation attacks. We show that resistors with resistance values differing by 5 orders of magnitude, diodes with variable turn-on voltages and reverse saturation currents, and FETs with adjustable conduction type, threshold voltages, and switching characteristics can be optically camouflaged to look exactly similar by engineering TMO/TMD heterostructures, allowing hardware obfuscation of both digital and analog circuits. Since this 2D heterostructure devices family is intrinsically camouflaged, NAND/NOR/AND/OR gates in the circuit can be obfuscated with significantly less area overhead, allowing 100% logic obfuscation compared to only 5% for complementary metal oxide semiconductor (CMOS)-based camouflaging. Finally, we demonstrate that the largest benchmarking circuit from ISCAS'85, comprised of more than 4000 logic gates when obfuscated with the CMOS-based technique, is successfully decamouflaged by SAT attack in <40 min; whereas, it renders to be invulnerable even in more than 10 h when camouflaged with 2D heterostructure devices, thereby corroborating our hypothesis of high resilience against RE. Our approach of connecting material properties to innovative devices to secure circuits can be considered as a one of a kind demonstration, highlighting the benefits of cross-layer optimization.
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Affiliation(s)
- Akshay Wali
- Department of Electrical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Shamik Kundu
- Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, Texas 75080, United States
| | - Andrew J Arnold
- Department of Electrical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Guangwei Zhao
- Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, Texas 75080, United States
| | - Kanad Basu
- Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, Texas 75080, United States
| | - Saptarshi Das
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania 16802, United States
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40
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Hassanzadeh P. The capabilities of nanoelectronic 2-D materials for bio-inspired computing and drug delivery indicate their significance in modern drug design. Life Sci 2021; 279:119272. [PMID: 33631171 DOI: 10.1016/j.lfs.2021.119272] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/13/2022]
Abstract
Remarkable advancements in the computational techniques and nanoelectronics have attracted considerable interests for development of highly-sophisticated materials (Ms) including the theranostics with optimal characteristics and innovative delivery systems. Analyzing the huge amounts of multivariate data and solving the newly-emerged complicated problems including the healthcare-related ones have created increasing demands for improving the computational speed and minimizing the consumption of energy. Shifting towards the non-von Neumann approaches enables performing specific computational tasks and optimizing the processing of signals. Besides usefulness for neuromorphic computing and increasing the efficiency of computation energy, 2-D electronic Ms are capable of optical sensing with ultra-fast and ultra-sensitive responses, mimicking the neurons, detection of pathogens or biomolecules, and prediction of the progression of diseases, assessment of the pharmacokinetics/pharmacodynamics of therapeutic candidates, mimicking the dynamics of the release of neurotransmitters or fluxes of ions that might provide a deeper knowledge about the computations and information flow in the brain, and development of more effective treatment protocols with improved outcomes. 2-D Ms appear as the major components of the next-generation electronically-enabled devices for highly-advanced computations, bio-imaging, diagnostics, tissue engineering, and designing smart systems for site-specific delivery of therapeutics that might result in the reduced adverse effects of drugs and improved patient compliance. This manuscript highlights the significance of 2-D Ms in the neuromorphic computing, optimizing the energy efficiency of the multi-step computations, providing novel architectures or multi-functional systems, improved performance of a variety of devices and bio-inspired functionalities, and delivery of theranostics.
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Affiliation(s)
- Parichehr Hassanzadeh
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran.
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41
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Sebastian A, Pendurthi R, Choudhury TH, Redwing JM, Das S. Benchmarking monolayer MoS 2 and WS 2 field-effect transistors. Nat Commun 2021; 12:693. [PMID: 33514710 PMCID: PMC7846590 DOI: 10.1038/s41467-020-20732-w] [Citation(s) in RCA: 113] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 12/17/2020] [Indexed: 11/09/2022] Open
Abstract
Here we benchmark device-to-device variation in field-effect transistors (FETs) based on monolayer MoS2 and WS2 films grown using metal-organic chemical vapor deposition process. Our study involves 230 MoS2 FETs and 160 WS2 FETs with channel lengths ranging from 5 μm down to 100 nm. We use statistical measures to evaluate key FET performance indicators for benchmarking these two-dimensional (2D) transition metal dichalcogenide (TMD) monolayers against existing literature as well as ultra-thin body Si FETs. Our results show consistent performance of 2D FETs across 1 × 1 cm2 chips owing to high quality and uniform growth of these TMDs followed by clean transfer onto device substrates. We are able to demonstrate record high carrier mobility of 33 cm2 V-1 s-1 in WS2 FETs, which is a 1.5X improvement compared to the best reported in the literature. Our experimental demonstrations confirm the technological viability of 2D FETs in future integrated circuits.
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Affiliation(s)
- Amritanand Sebastian
- Department of Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Rahul Pendurthi
- Department of Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Tanushree H Choudhury
- 2D Crystal Consortium-Materials Innovation Platform (2DCC-MIP), Penn State University, University Park, PA, 16802, USA
| | - Joan M Redwing
- 2D Crystal Consortium-Materials Innovation Platform (2DCC-MIP), Penn State University, University Park, PA, 16802, USA.,Department of Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA.,Materials Research Institute, Penn State University, University Park, PA, 16802, USA
| | - Saptarshi Das
- Department of Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA. .,Department of Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA. .,Materials Research Institute, Penn State University, University Park, PA, 16802, USA.
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42
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Song C, Noh G, Kim TS, Kang M, Song H, Ham A, Jo MK, Cho S, Chai HJ, Cho SR, Cho K, Park J, Song S, Song I, Bang S, Kwak JY, Kang K. Growth and Interlayer Engineering of 2D Layered Semiconductors for Future Electronics. ACS NANO 2020; 14:16266-16300. [PMID: 33301290 DOI: 10.1021/acsnano.0c06607] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Layered materials that do not form a covalent bond in a vertical direction can be prepared in a few atoms to one atom thickness without dangling bonds. This distinctive characteristic of limiting thickness around the sub-nanometer level allowed scientists to explore various physical phenomena in the quantum realm. In addition to the contribution to fundamental science, various applications were proposed. Representatively, they were suggested as a promising material for future electronics. This is because (i) the dangling-bond-free nature inhibits surface scattering, thus carrier mobility can be maintained at sub-nanometer range; (ii) the ultrathin nature allows the short-channel effect to be overcome. In order to establish fundamental discoveries and utilize them in practical applications, appropriate preparation methods are required. On the other hand, adjusting properties to fit the desired application properly is another critical issue. Hence, in this review, we first describe the preparation method of layered materials. Proper growth techniques for target applications and the growth of emerging materials at the beginning stage will be extensively discussed. In addition, we suggest interlayer engineering via intercalation as a method for the development of artificial crystal. Since infinite combinations of the host-intercalant combination are possible, it is expected to expand the material system from the current compound system. Finally, inevitable factors that layered materials must face to be used as electronic applications will be introduced with possible solutions. Emerging electronic devices realized by layered materials are also discussed.
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Affiliation(s)
- Chanwoo Song
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Gichang Noh
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
- Center for Electronic Materials, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
| | - Tae Soo Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Minsoo Kang
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Hwayoung Song
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Ayoung Ham
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Min-Kyung Jo
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
- Operando Methodology and Measurement Team, Interdisciplinary Materials Measurement Institute, Korea Research Institute of Standards and Science (KRISS), Daejeon 34113, Korea
| | - Seorin Cho
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Hyun-Jun Chai
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Seong Rae Cho
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Kiwon Cho
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jeongwon Park
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Seungwoo Song
- Operando Methodology and Measurement Team, Interdisciplinary Materials Measurement Institute, Korea Research Institute of Standards and Science (KRISS), Daejeon 34113, Korea
| | - Intek Song
- Department of Applied Chemistry, Andong National University, Andong 36728, Korea
| | - Sunghwan Bang
- Materials & Production Engineering Research Institute, LG Electronics, Pyeongtaek-si 17709, Korea
| | - Joon Young Kwak
- Center for Electronic Materials, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea
| | - Kibum Kang
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
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43
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Cheng Y, Shan K, Xu Y, Yang J, He J, Jiang J. Hardware implementation of photoelectrically modulated dendritic arithmetic and spike-timing-dependent plasticity enabled by an ion-coupling gate-tunable vertical 0D-perovskite/2D-MoS 2 hybrid-dimensional van der Waals heterostructure. NANOSCALE 2020; 12:21798-21811. [PMID: 33103690 DOI: 10.1039/d0nr04950f] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Brain-inspired nanodevices have been demonstrated to possess outstanding characteristics for implementing neuromorphic computing. Among these devices, photoelectrically modulated neuromorphic transistors are regarded as the basic building blocks for applications in emerging brain-like devices. However, to date, efficient optoelectronic-hybrid neuromorphic devices are still lacking. Because conventional transistors based on mono-semiconductor materials cannot absorb adequate light to ensure efficient light-matter interactions, they pose significant challenges to the synchronous processing of photoelectric information. Here, a novel photoelectrically modulated neuromorphic device based on an ion-coupling gate-tunable vertical 0D-CsPbBr3-quantum-dots/2D-MoS2 hybrid-dimensional van der Waals heterojunction is demonstrated by using a polymer ion gel electrolyte as the gate dielectric. A super-efficient heterojunction interface for photo-carrier transport is developed by integrating CsPbBr3 quantum dots with 2D-layered MoS2 semiconductors. We experimentally demonstrate that the drain-source current can be modulated by applying spikes to the drain and gate terminals, and the conductance can also be tuned by external light stimulus. Most importantly, photoelectrically modulated spiking Boolean logics, dendritic integrations in both temporal and spatial modes, and Hebbian learning rules can be successfully mimicked in our proposed hybrid-dimensional device using this intriguing optical and electrical synergy approach. These results suggest that the proposed device has great potential in intelligent cognitive systems and neuromorphic computing applications.
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Affiliation(s)
- Yongchao Cheng
- Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha 410083, China.
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Migliato Marega G, Zhao Y, Avsar A, Wang Z, Tripathi M, Radenovic A, Kis A. Logic-in-memory based on an atomically thin semiconductor. Nature 2020; 587:72-77. [PMID: 33149289 PMCID: PMC7116757 DOI: 10.1038/s41586-020-2861-0] [Citation(s) in RCA: 120] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 08/26/2020] [Indexed: 11/12/2022]
Abstract
The growing importance of applications based on machine learning is driving the need to develop dedicated, energy-efficient electronic hardware. Compared with von-Neumann architectures, brain-inspired in-memory computing uses the same basic device structure for logic operations and data storage1–3, thus promising to reduce the energy cost of data-centric computing significantly4. While there is ample research focused on exploring new device architectures, the engineering of material platforms suitable for such device designs remains a challenge. Two-dimensional materials5,6 such as semiconducting MoS2 could stand out as a promising candidate to face this obstacle thanks to their exceptional electrical and mechanical properties7–9. Here, we explore large-area grown MoS2 as an active channel material for developing logic-in-memory devices and circuits based on floating-gate field-effect transistors (FGFET). The conductance of our FGFETs can be precisely and continuously tuned, allowing us to use them as building blocks for reconfigurable logic circuits where logic operations can be directly performed using the memory elements. After demonstrating a programmable NOR gate, we show that this design can be simply extended to implement more complex programmable logic and functionally complete sets of functions. Our findings highlight the potential of atomically thin semiconductors for the development of next-generation low-power electronics.
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Affiliation(s)
- Guilherme Migliato Marega
- Electrical Engineering Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Institute of Materials Science and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Yanfei Zhao
- Electrical Engineering Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Institute of Materials Science and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ahmet Avsar
- Electrical Engineering Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Institute of Materials Science and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Zhenyu Wang
- Electrical Engineering Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Institute of Materials Science and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mukesh Tripathi
- Electrical Engineering Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Institute of Materials Science and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Aleksandra Radenovic
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Andras Kis
- Electrical Engineering Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. .,Institute of Materials Science and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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Cheng Y, Li H, Liu B, Jiang L, Liu M, Huang H, Yang J, He J, Jiang J. Vertical 0D-Perovskite/2D-MoS 2 van der Waals Heterojunction Phototransistor for Emulating Photoelectric-Synergistically Classical Pavlovian Conditioning and Neural Coding Dynamics. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e2005217. [PMID: 33035390 DOI: 10.1002/smll.202005217] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 09/10/2020] [Indexed: 06/11/2023]
Abstract
Optoelectronic-neuromorphic transistors are vital for next-generation nanoscale brain-like computational systems. However, the hardware implementation of optoelectronic-neuromorphic devices, which are based on conventional transistor architecture, faces serious challenges with respect to the synchronous processing of photoelectric information. This is because mono-semiconductor material cannot absorb adequate light to ensure efficient light-matter interactions. In this work, a novel neuromorphic-photoelectric device of vertical van der Waals heterojunction phototransistors based on a colloidal 0D-CsPbBr3 -quantum-dots/2D-MoS2 heterojunction channel is proposed using a polymer ion gel electrolyte as the gate dielectric. A highly efficient photocarrier transport interface is established by introducing colloidal perovskite quantum dots with excellent light absorption capabilities on the 2D-layered MoS2 semiconductor with strong carrier transport abilities. The device exhibits not only high photoresponsivity but also fundamental synaptic characteristics, such as excitatory postsynaptic current, paired-pulse facilitation, dynamic temporal filter, and light-tunable synaptic plasticity. More importantly, efficiency-adjustable photoelectronic Pavlovian conditioning and photoelectronic hybrid neuronal coding behaviors can be successfully implemented using the optical and electrical synergy approach. The results suggest that the proposed device has potential for applications associated with next-generation brain-like photoelectronic human-computer interactions and cognitive systems.
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Affiliation(s)
- Yongchao Cheng
- Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, 410083, China
| | - Huangjinwei Li
- Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, 410083, China
| | - Biao Liu
- Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, 410083, China
| | - Leyong Jiang
- School of Physics and Electronics, Hunan Normal University, Changsha, 410081, China
| | - Min Liu
- Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, 410083, China
| | - Han Huang
- Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, 410083, China
| | - Junliang Yang
- Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, 410083, China
| | - Jun He
- Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, 410083, China
| | - Jie Jiang
- Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, 410083, China
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Schranghamer TF, Oberoi A, Das S. Graphene memristive synapses for high precision neuromorphic computing. Nat Commun 2020; 11:5474. [PMID: 33122647 PMCID: PMC7596564 DOI: 10.1038/s41467-020-19203-z] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 09/29/2020] [Indexed: 11/08/2022] Open
Abstract
Memristive crossbar architectures are evolving as powerful in-memory computing engines for artificial neural networks. However, the limited number of non-volatile conductance states offered by state-of-the-art memristors is a concern for their hardware implementation since trained weights must be rounded to the nearest conductance states, introducing error which can significantly limit inference accuracy. Moreover, the incapability of precise weight updates can lead to convergence problems and slowdown of on-chip training. In this article, we circumvent these challenges by introducing graphene-based multi-level (>16) and non-volatile memristive synapses with arbitrarily programmable conductance states. We also show desirable retention and programming endurance. Finally, we demonstrate that graphene memristors enable weight assignment based on k-means clustering, which offers greater computing accuracy when compared with uniform weight quantization for vector matrix multiplication, an essential component for any artificial neural network.
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Affiliation(s)
- Thomas F Schranghamer
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16802, USA
| | - Aaryan Oberoi
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16802, USA
| | - Saptarshi Das
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16802, USA.
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, PA, 16802, USA.
- Materials Research Institute, Pennsylvania State University, University Park, PA, 16802, USA.
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Park S, Jeong Y, Jin HJ, Park J, Jang H, Lee S, Huh W, Cho H, Shin HG, Kim K, Lee CH, Choi S, Im S. Nonvolatile and Neuromorphic Memory Devices Using Interfacial Traps in Two-Dimensional WSe 2/MoTe 2 Stack Channel. ACS NANO 2020; 14:12064-12071. [PMID: 32816452 DOI: 10.1021/acsnano.0c05393] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Very recently, stacked two-dimensional materials have been studied, focusing on the van der Waals interaction at their stack junction interface. Here, we report field effect transistors (FETs) with stacked transition metal dichalcogenide (TMD) channels, where the heterojunction interface between two TMDs appears useful for nonvolatile or neuromorphic memory FETs. A few nanometer-thin WSe2 and MoTe2 flakes are vertically stacked on the gate dielectric, and bottom p-MoTe2 performs as a channel for hole transport. Interestingly, the WSe2/MoTe2 stack interface functions as a hole trapping site where traps behave in a nonvolatile manner, although trapping/detrapping can be controlled by gate voltage (VGS). Memory retention after high VGS pulse appears longer than 10000 s, and the Program/Erase ratio in a drain current is higher than 200. Moreover, the traps are delicately controllable even with small VGS, which indicates that a neuromorphic memory is also possible with our heterojunction stack FETs. Our stack channel FET demonstrates neuromorphic memory behavior of ∼94% recognition accuracy.
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Affiliation(s)
- Sam Park
- Van der Waals Materials Research Center, Department of Physics, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Yeonsu Jeong
- Van der Waals Materials Research Center, Department of Physics, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Hye-Jin Jin
- Van der Waals Materials Research Center, Department of Physics, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Junkyu Park
- The school of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Hyenam Jang
- The school of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Sol Lee
- Van der Waals Materials Research Center, Department of Physics, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Woong Huh
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Hyunmin Cho
- Van der Waals Materials Research Center, Department of Physics, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Hyung Gon Shin
- Van der Waals Materials Research Center, Department of Physics, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Kwanpyo Kim
- Van der Waals Materials Research Center, Department of Physics, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Chul-Ho Lee
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Shinhyun Choi
- The school of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Seongil Im
- Van der Waals Materials Research Center, Department of Physics, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
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Abstract
In this article, we adopt a radical approach for next generation ultra-low-power sensor design by embracing the evolutionary success of animals with extraordinary sensory information processing capabilities that allow them to survive in extreme and resource constrained environments. Stochastic resonance (SR) is one of those astounding phenomena, where noise, which is considered detrimental for electronic circuits and communication systems, plays a constructive role in the detection of weak signals. Here, we show SR in a photodetector based on monolayer MoS2 for detecting ultra-low-intensity subthreshold optical signals from a distant light emitting diode (LED). We demonstrate that weak periodic LED signals, which are otherwise undetectable, can be detected by a MoS2 photodetector in the presence of a finite and optimum amount of white Gaussian noise at a frugal energy expenditure of few tens of nano-Joules. The concept of SR is generic in nature and can be extended beyond photodetector to any other sensors. Here, the authors take advantage of stochastic resonance in a photodetector based on monolayer MoS2 for measuring otherwise undetectable, ultra-low-intensity, subthreshold optical signals from a distant light emitting diode in the presence of a finite and optimum amount of white Gaussian noise.
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Sangwan VK, Hersam MC. Neuromorphic nanoelectronic materials. NATURE NANOTECHNOLOGY 2020; 15:517-528. [PMID: 32123381 DOI: 10.1038/s41565-020-0647-z] [Citation(s) in RCA: 187] [Impact Index Per Article: 46.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 01/23/2020] [Indexed: 05/10/2023]
Abstract
Memristive and nanoionic devices have recently emerged as leading candidates for neuromorphic computing architectures. While top-down fabrication based on conventional bulk materials has enabled many early neuromorphic devices and circuits, bottom-up approaches based on low-dimensional nanomaterials have shown novel device functionality that often better mimics a biological neuron. In addition, the chemical, structural and compositional tunability of low-dimensional nanomaterials coupled with the permutational flexibility enabled by van der Waals heterostructures offers significant opportunities for artificial neural networks. In this Review, we present a critical survey of emerging neuromorphic devices and architectures enabled by quantum dots, metal nanoparticles, polymers, nanotubes, nanowires, two-dimensional layered materials and van der Waals heterojunctions with a particular emphasis on bio-inspired device responses that are uniquely enabled by low-dimensional topology, quantum confinement and interfaces. We also provide a forward-looking perspective on the opportunities and challenges of neuromorphic nanoelectronic materials in comparison with more mature technologies based on traditional bulk electronic materials.
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Affiliation(s)
- Vinod K Sangwan
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Mark C Hersam
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA.
- Department of Chemistry, Northwestern University, Evanston, IL, USA.
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA.
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50
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Beck ME, Hersam MC. Emerging Opportunities for Electrostatic Control in Atomically Thin Devices. ACS NANO 2020; 14:6498-6518. [PMID: 32463222 DOI: 10.1021/acsnano.0c03299] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Electrostatic control of charge carrier concentration underlies the field-effect transistor (FET), which is among the most ubiquitous devices in the modern world. As transistors and related electronic devices have been miniaturized to the nanometer scale, electrostatics have become increasingly important, leading to progressively sophisticated device geometries such as the finFET. With the advent of atomically thin materials in which dielectric screening lengths are greater than device physical dimensions, qualitatively different opportunities emerge for electrostatic control. In this Review, recent demonstrations of unconventional electrostatic modulation in atomically thin materials and devices are discussed. By combining low dielectric screening with the other characteristics of atomically thin materials such as relaxed requirements for lattice matching, quantum confinement of charge carriers, and mechanical flexibility, high degrees of electrostatic spatial inhomogeneity can be achieved, which enables a diverse range of gate-tunable properties that are useful in logic, memory, neuromorphic, and optoelectronic technologies.
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Affiliation(s)
- Megan E Beck
- Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Mark C Hersam
- Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Department of Chemistry, Northwestern University, Evanston, Illinois 60208, United States
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, Illinois 60208, United States
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