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Gao B, Wang W, Meng Y, Du C, Long Y, Zhang Y, Shao H, Lai Z, Wang W, Xie P, Yip S, Zhong X, Ho JC. Electrical Polarity Modulation in V-Doped Monolayer WS 2 for Homogeneous CMOS Inverters. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2402217. [PMID: 38924273 DOI: 10.1002/smll.202402217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 05/14/2024] [Indexed: 06/28/2024]
Abstract
As demand for higher integration density and smaller devices grows, silicon-based complementary metal-oxide-semiconductor (CMOS) devices will soon reach their ultimate limits. 2D transition metal dichalcogenides (TMDs) semiconductors, known for excellent electrical performance and stable atomic structure, are seen as promising materials for future integrated circuits. However, controlled and reliable doping of 2D TMDs, a key step for creating homogeneous CMOS logic components, remains a challenge. In this study, a continuous electrical polarity modulation of monolayer WS2 from intrinsic n-type to ambipolar, then to p-type, and ultimately to a quasi-metallic state is achieved simply by introducing controllable amounts of vanadium (V) atoms into the WS2 lattice as p-type dopants during chemical vapor deposition (CVD). The achievement of purely p-type field-effect transistors (FETs) is particularly noteworthy based on the 4.7 at% V-doped monolayer WS2, demonstrating a remarkable on/off current ratio of 105. Expanding on this triumph, the first initial prototype of ultrathin homogeneous CMOS inverters based on monolayer WS2 is being constructed. These outcomes validate the feasibility of constructing homogeneous CMOS devices through the atomic doping process of 2D materials, marking a significant milestone for the future development of integrated circuits.
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Affiliation(s)
- Boxiang Gao
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Weijun Wang
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - You Meng
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Congcong Du
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
- Qingyuan Innovation Laboratory, Quanzhou, 362801, China
| | - Yunchen Long
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Yuxuan Zhang
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - He Shao
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Zhengxun Lai
- College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha, 410082, China
| | - Wei Wang
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Pengshan Xie
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - SenPo Yip
- Institute for Materials Chemistry and Engineering, Kyushu University, Fukuoka, 816-8580, Japan
| | - Xiaoyan Zhong
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
- City University of Hong Kong Matter Science Research Institute (Futian, Shenzhen), Shenzhen, 518048, China
- Nanomanufacturing Laboratory (NML), City University of Hong Kong Shenzhen Research Institute, Shenzhen, 518057, China
| | - Johnny C Ho
- Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
- Institute for Materials Chemistry and Engineering, Kyushu University, Fukuoka, 816-8580, Japan
- State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Hong Kong SAR, 999077, China
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2
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Sakib NU, Karim Sadaf MU, Pannone A, Ghosh S, Zheng Y, Ravichandran H, Das S. A Crayfish-Inspired Sensor Fusion Platform for Super Additive Integration of Visual, Chemical, and Tactile Information. NANO LETTERS 2024; 24:6948-6956. [PMID: 38810209 DOI: 10.1021/acs.nanolett.4c01187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
The concept of cross-sensor modulation, wherein one sensor modality can influence another's response, is often overlooked in traditional sensor fusion architectures, leading to missed opportunities for enhancing data accuracy and robustness. In contrast, biological systems, such as aquatic animals like crayfish, demonstrate superior sensor fusion through multisensory integration. These organisms adeptly integrate visual, tactile, and chemical cues to perform tasks such as evading predators and locating prey. Drawing inspiration from this, we propose a neuromorphic platform that integrates graphene-based chemitransistors, monolayer molybdenum disulfide (MoS2) based photosensitive memtransistors, and triboelectric tactile sensors to achieve "Super-Additive" responses to weak chemical, visual, and tactile cues and demonstrate contextual response modulation, also referred to as the "Inverse Effectiveness Effect." We hold the view that integrating bio-inspired sensor fusion principles across various modalities holds promise for a wide range of applications.
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Affiliation(s)
- Najam U Sakib
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Muhtasim Ul Karim Sadaf
- 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
| | - Subir Ghosh
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Yikai Zheng
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Harikrishnan Ravichandran
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
- Electrical Engineering, 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
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3
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Sun Y, Wang H, Xie D. Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications. NANO-MICRO LETTERS 2024; 16:211. [PMID: 38842588 PMCID: PMC11156833 DOI: 10.1007/s40820-024-01445-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 05/14/2024] [Indexed: 06/07/2024]
Abstract
Manipulating the expression of synaptic plasticity of neuromorphic devices provides fascinating opportunities to develop hardware platforms for artificial intelligence. However, great efforts have been devoted to exploring biomimetic mechanisms of plasticity simulation in the last few years. Recent progress in various plasticity modulation techniques has pushed the research of synaptic electronics from static plasticity simulation to dynamic plasticity modulation, improving the accuracy of neuromorphic computing and providing strategies for implementing neuromorphic sensing functions. Herein, several fascinating strategies for synaptic plasticity modulation through chemical techniques, device structure design, and physical signal sensing are reviewed. For chemical techniques, the underlying mechanisms for the modification of functional materials were clarified and its effect on the expression of synaptic plasticity was also highlighted. Based on device structure design, the reconfigurable operation of neuromorphic devices was well demonstrated to achieve programmable neuromorphic functions. Besides, integrating the sensory units with neuromorphic processing circuits paved a new way to achieve human-like intelligent perception under the modulation of physical signals such as light, strain, and temperature. Finally, considering that the relevant technology is still in the basic exploration stage, some prospects or development suggestions are put forward to promote the development of neuromorphic devices.
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Affiliation(s)
- Yilin Sun
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, 100081, People's Republic of China.
| | - Huaipeng Wang
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, People's Republic of China
| | - Dan Xie
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, People's Republic of China.
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4
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Qin T, Zhao X, Sui Y, Wang D, Chen W, Zhang Y, Luo S, Pan W, Guo Z, Leung DYC. Heterointerfaces: Unlocking Superior Capacity and Rapid Mass Transfer Dynamics in Energy Storage Electrodes. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2402644. [PMID: 38822769 DOI: 10.1002/adma.202402644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/05/2024] [Indexed: 06/03/2024]
Abstract
Heterogeneous electrode materials possess abundant heterointerfaces with a localized "space charge effect", which enhances capacity output and accelerates mass/charge transfer dynamics in energy storage devices (ESDs). These promising features open new possibilities for demanding applications such as electric vehicles, grid energy storage, and portable electronics. However, the fundamental principles and working mechanisms that govern heterointerfaces are not yet fully understood, impeding the rational design of electrode materials. In this study, the heterointerface evolution during charging and discharging process as well as the intricate interaction between heterointerfaces and charge/mass transport phenomena, is systematically discussed. Guidelines along with feasible strategies for engineering structural heterointerfaces to address specific challenges encountered in various application scenarios, are also provided. This review offers innovative solutions for the development of heterogeneous electrode materials, enabling more efficient energy storage beyond conventional electrochemistry. Furthermore, it provides fresh insights into the advancement of clean energy conversion and storage technologies. This review contributes to the knowledge and understanding of heterointerfaces, paving the way for the design and optimization of next-generation energy storage materials for a sustainable future.
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Affiliation(s)
- Tingting Qin
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China
| | - Xiaolong Zhao
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China
| | - Yiming Sui
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331-4003, USA
| | - Dong Wang
- Key Laboratory of Automobile Materials of MOE School of Materials Science and Engineering and Jilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy Materials, Jilin University, Changchun, 130013, China
| | - Weicheng Chen
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China
| | - Yingguang Zhang
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China
| | - Shijing Luo
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China
| | - Wending Pan
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China
| | - Zhenbin Guo
- Institute of Semiconductor Manufacturing Research, Shenzhen University, Shenzhen, 518060, China
| | - Dennis Y C Leung
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China
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Wali A, Ravichandran H, Das S. A 2D Cryptographic Hash Function Incorporating Homomorphic Encryption for Secure Digital Signatures. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2400661. [PMID: 38373292 DOI: 10.1002/adma.202400661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Indexed: 02/21/2024]
Abstract
User authentication is a critical aspect of any information exchange system which verifies the identities of individuals seeking access to sensitive information. Conventionally, this approachrelies on establishing robust digital signature protocols which employ asymmetric encryption techniques involving a key pair consisting of a public key and its matching private key. In this article, a user verification platform constructed using integrated circuits (ICs) with atomically thin two-dimensional (2D) monolayer molybdenum disulfide (MoS2 ) memtransistors is presented. First, generation of secure cryptographic keys is demonstrated by exploiting the inherent stochasticity of carrier trapping and detrapping at the 2D/oxide interface trap sites. Subsequently, the ability to manipulate the functionality of logical NOR is leveraged to create a secure one-way hash function which when homomorphically operated upon with NAND, XOR, OR, NOT, and AND logic circuits generate distinct digital signatures. These signatures when subsequently decrypted, verify the authenticity of the receiver while ensuring complete preservation of data integrity and confidentiality as the underlying information is never revealed. Finally, the advantages of implementing a NOR-based hashing techniques in comparison to the conventional XOR-based encryption method are established. This demonstration highlights the potential of 2D-based ICs in developing critical hardware information security primitives.
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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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6
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Liu A, Zhang X, Liu Z, Li Y, Peng X, Li X, Qin Y, Hu C, Qiu Y, Jiang H, Wang Y, Li Y, Tang J, Liu J, Guo H, Deng T, Peng S, Tian H, Ren TL. The Roadmap of 2D Materials and Devices Toward Chips. NANO-MICRO LETTERS 2024; 16:119. [PMID: 38363512 PMCID: PMC10873265 DOI: 10.1007/s40820-023-01273-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/30/2023] [Indexed: 02/17/2024]
Abstract
Due to the constraints imposed by physical effects and performance degradation, silicon-based chip technology is facing certain limitations in sustaining the advancement of Moore's law. Two-dimensional (2D) materials have emerged as highly promising candidates for the post-Moore era, offering significant potential in domains such as integrated circuits and next-generation computing. Here, in this review, the progress of 2D semiconductors in process engineering and various electronic applications are summarized. A careful introduction of material synthesis, transistor engineering focused on device configuration, dielectric engineering, contact engineering, and material integration are given first. Then 2D transistors for certain electronic applications including digital and analog circuits, heterogeneous integration chips, and sensing circuits are discussed. Moreover, several promising applications (artificial intelligence chips and quantum chips) based on specific mechanism devices are introduced. Finally, the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed, and potential development pathways or roadmaps are further speculated and outlooked.
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Affiliation(s)
- Anhan Liu
- School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100049, People's Republic of China
| | - Xiaowei Zhang
- School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100049, People's Republic of China
| | - Ziyu Liu
- School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Yuning Li
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, People's Republic of China
| | - Xueyang Peng
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, People's Republic of China
- School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Xin Li
- State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan, 030051, People's Republic of China
| | - Yue Qin
- State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan, 030051, People's Republic of China
| | - Chen Hu
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, People's Republic of China
- School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Yanqing Qiu
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, People's Republic of China
- School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Han Jiang
- School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Yang Wang
- School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Yifan Li
- School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100049, People's Republic of China
| | - Jun Tang
- State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan, 030051, People's Republic of China
| | - Jun Liu
- State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan, 030051, People's Republic of China
| | - Hao Guo
- State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan, 030051, People's Republic of China.
| | - Tao Deng
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, People's Republic of China.
| | - Songang Peng
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, People's Republic of China.
- IMECAS-HKUST-Joint Laboratory of Microelectronics, Beijing, 100029, People's Republic of China.
| | - He Tian
- School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100049, People's Republic of China.
| | - Tian-Ling Ren
- School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100049, People's Republic of China.
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7
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Jayachandran D, Pendurthi R, Sadaf MUK, Sakib NU, Pannone A, Chen C, Han Y, Trainor N, Kumari S, Mc Knight TV, Redwing JM, Yang Y, Das S. Three-dimensional integration of two-dimensional field-effect transistors. Nature 2024; 625:276-281. [PMID: 38200300 DOI: 10.1038/s41586-023-06860-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 11/10/2023] [Indexed: 01/12/2024]
Abstract
In the field of semiconductors, three-dimensional (3D) integration not only enables packaging of more devices per unit area, referred to as 'More Moore'1 but also introduces multifunctionalities for 'More than Moore'2 technologies. Although silicon-based 3D integrated circuits are commercially available3-5, there is limited effort on 3D integration of emerging nanomaterials6,7 such as two-dimensional (2D) materials despite their unique functionalities7-10. Here we demonstrate (1) wafer-scale and monolithic two-tier 3D integration based on MoS2 with more than 10,000 field-effect transistors (FETs) in each tier; (2) three-tier 3D integration based on both MoS2 and WSe2 with about 500 FETs in each tier; and (3) two-tier 3D integration based on 200 scaled MoS2 FETs (channel length, LCH = 45 nm) in each tier. We also realize a 3D circuit and demonstrate multifunctional capabilities, including sensing and storage. We believe that our demonstrations will serve as the foundation for more sophisticated, highly dense and functionally divergent integrated circuits with a larger number of tiers integrated monolithically in the third dimension.
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Affiliation(s)
- Darsith Jayachandran
- Engineering Science and Mechanics, Penn State University, University Park, PA, USA.
| | - Rahul Pendurthi
- Engineering Science and Mechanics, Penn State University, University Park, PA, USA.
| | | | - Najam U Sakib
- Engineering Science and Mechanics, Penn State University, University Park, PA, USA
| | - Andrew Pannone
- Engineering Science and Mechanics, Penn State University, University Park, PA, USA
| | - Chen Chen
- 2D Crystal Consortium Materials Innovation Platform, Penn State University, University Park, PA, USA
| | - Ying Han
- Engineering Science and Mechanics, Penn State University, University Park, PA, USA
| | - Nicholas Trainor
- 2D Crystal Consortium Materials Innovation Platform, Penn State University, University Park, PA, USA
- Materials Science and Engineering, Penn State University, University Park, PA, USA
| | - Shalini Kumari
- 2D Crystal Consortium Materials Innovation Platform, Penn State University, University Park, PA, USA
- Materials Science and Engineering, Penn State University, University Park, PA, USA
| | - Thomas V Mc Knight
- 2D Crystal Consortium Materials Innovation Platform, Penn State University, University Park, PA, USA
- Materials Science and Engineering, Penn State University, University Park, PA, USA
| | - Joan M Redwing
- 2D Crystal Consortium Materials Innovation Platform, Penn State University, University Park, PA, USA
- Materials Science and Engineering, Penn State University, University Park, PA, USA
- Materials Research Institute, Penn State University, University Park, PA, USA
| | - Yang Yang
- Engineering Science and Mechanics, Penn State University, University Park, PA, USA
- Materials Research Institute, Penn State University, University Park, PA, USA
- Nuclear Engineering, Penn State University, University Park, PA, USA
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, PA, USA.
- Materials Science and Engineering, Penn State University, University Park, PA, USA.
- Materials Research Institute, Penn State University, University Park, PA, USA.
- Electrical Engineering, Penn State University, University Park, PA, USA.
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8
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Hu L, Li X, Guo X, Xu M, Shi Y, Herve NB, Xiang R, Zhang Q. Electret Modulation Strategy to Enhance the Photosensitivity Performance of Two-Dimensional Molybdenum Sulfide. ACS APPLIED MATERIALS & INTERFACES 2023; 15:59704-59713. [PMID: 38087993 DOI: 10.1021/acsami.3c14836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
Due to the limited light absorption efficiency of atomic thickness layers and the existence of quenching effects, photodetectors solely made of transition metal dichalcogenides (TMDs) have exhibited an unsatisfactory detection performance. In this article, electret/TMD hybridized devices were proposed by vertically coupling a MoS2 channel and the PTFE film, which reveals an optimized photodetection behavior. Negative charges were generated in the PTFE layer through the corona charging method, akin to applying a negative bias on the MoS2 channel in lieu of a traditional voltage-driven back gate. Under a charging voltage of -6 kV, PTFE/MoS2 devices reveal improved photodetection performance (Rhybrid = 67.95A/W versus Ronly = 3.37 A/W, at 470 nm, 1.20 mW cm-2) and faster recovery speed (τd(hybrid) = 2000 ms versus τd(only) = 2900 ms) compared to those bare MoS2 counterparts. The optimal detection performance (2 orders of magnitude) was obtained when the charging voltage was -2 kV, limited by the minimum of the carrier density in MoS2 channels. This study provides an alternative strategy to optimize optoelectronic devices based on the 2D components through non-voltage-driven gating.
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Affiliation(s)
- Lian Hu
- Center for Advanced Optoelectronic Materials, College of Materials and Environmental Engineering, Hangzhou Dianzi University (HDU), Hangzhou 310018, China
| | - Xin Li
- Center for Advanced Optoelectronic Materials, College of Materials and Environmental Engineering, Hangzhou Dianzi University (HDU), Hangzhou 310018, China
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province, Hangzhou Dianzi University (HDU), Hangzhou 310018, P. R. China
| | - Xinyu Guo
- Center for Advanced Optoelectronic Materials, College of Materials and Environmental Engineering, Hangzhou Dianzi University (HDU), Hangzhou 310018, China
| | - Minxuan Xu
- Center for Advanced Optoelectronic Materials, College of Materials and Environmental Engineering, Hangzhou Dianzi University (HDU), Hangzhou 310018, China
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province, Hangzhou Dianzi University (HDU), Hangzhou 310018, P. R. China
| | - Yueqin Shi
- Center for Advanced Optoelectronic Materials, College of Materials and Environmental Engineering, Hangzhou Dianzi University (HDU), Hangzhou 310018, China
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province, Hangzhou Dianzi University (HDU), Hangzhou 310018, P. R. China
| | - Nduwarugira B Herve
- School of Mechanical Engineering, Zhejiang University, Hangzhou 310003, China
| | - Rong Xiang
- School of Mechanical Engineering, Zhejiang University, Hangzhou 310003, China
| | - Qi Zhang
- Center for Advanced Optoelectronic Materials, College of Materials and Environmental Engineering, Hangzhou Dianzi University (HDU), Hangzhou 310018, China
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province, Hangzhou Dianzi University (HDU), Hangzhou 310018, P. R. China
- School of Mechanical Engineering, Zhejiang University, Hangzhou 310003, China
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9
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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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10
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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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11
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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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12
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Ravichandran H, Sen D, Wali A, Schranghamer TF, Trainor N, Redwing JM, Ray B, Das S. A Peripheral-Free True Random Number Generator Based on Integrated Circuits Enabled by Atomically Thin Two-Dimensional Materials. ACS NANO 2023; 17:16817-16826. [PMID: 37616285 DOI: 10.1021/acsnano.3c03581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
A true random number generator (TRNG) is essential to ensure information security for Internet of Things (IoT) edge devices. While pseudorandom number generators (PRNGs) have been instrumental, their deterministic nature limits their application in security-sensitive scenarios. In contrast, hardware-based TRNGs derived from physically unpredictable processes offer greater reliability. This study demonstrates a peripheral-free TRNG utilizing two cascaded three-stage inverters (TSIs) in conjunction with an XOR gate composed of monolayer molybdenum disulfide (MoS2) field-effect transistors (FETs) by exploiting the stochastic charge trapping and detrapping phenomena at and/or near the MoS2/dielectric interface. The entropy source passes the NIST SP800-90B tests with a minimum normalized entropy of 0.8780, while the generated bits pass the NIST SP800-22 randomness tests without any postprocessing. Moreover, the keys generated using these random bits are uncorrelated with near-ideal entropy, bit uniformity, and Hamming distances, exhibiting resilience against machine learning (ML) attacks, temperature variations, and supply bias fluctuations with a frugal energy expenditure of 30 pJ/bit. This approach offers an advantageous alternative to conventional silicon, memristive, and nanomaterial-based TRNGs as it obviates the need for extensive peripherals while harnessing the potential of atomically thin 2D materials in developing low-power TRNGs.
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Affiliation(s)
- Harikrishnan Ravichandran
- Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Dipanjan Sen
- Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Akshay Wali
- Electrical Engineering and Computer Science, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Thomas F Schranghamer
- Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Nicholas Trainor
- Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- 2D Crystal Consortium, Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Joan M Redwing
- Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- 2D Crystal Consortium, Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Biswajit Ray
- Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Saptarshi Das
- Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- 2D Crystal Consortium, Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Electrical Engineering and Computer Science, Pennsylvania State University, University Park, Pennsylvania 16802, United States
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13
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Ravichandran H, Knobloch T, Pannone A, Karl A, Stampfer B, Waldhoer D, Zheng Y, Sakib NU, Karim Sadaf MU, Pendurthi R, Torsi R, Robinson JA, Grasser T, Das S. Observation of Rich Defect Dynamics in Monolayer MoS 2. ACS NANO 2023. [PMID: 37490390 DOI: 10.1021/acsnano.2c12900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Defects play a pivotal role in limiting the performance and reliability of nanoscale devices. Field-effect transistors (FETs) based on atomically thin two-dimensional (2D) semiconductors such as monolayer MoS2 are no exception. Probing defect dynamics in 2D FETs is therefore of significant interest. Here, we present a comprehensive insight into various defect dynamics observed in monolayer MoS2 FETs at varying gate biases and temperatures. The measured source-to-drain currents exhibit random telegraph signals (RTS) owing to the transfer of charges between the semiconducting channel and individual defects. Based on the modeled temperature and gate bias dependence, oxygen vacancies or aluminum interstitials are probable defect candidates. Several types of RTSs are observed including anomalous RTS and giant RTS indicating local current crowding effects and rich defect dynamics in monolayer MoS2 FETs. This study explores defect dynamics in large area-grown monolayer MoS2 with ALD-grown Al2O3 as the gate dielectric.
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Affiliation(s)
- Harikrishnan Ravichandran
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Theresia Knobloch
- Institute for Microelectronics (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
| | - Andrew Pannone
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Alexander Karl
- Institute for Microelectronics (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
| | - Bernhard Stampfer
- Institute for Microelectronics (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
| | - Dominic Waldhoer
- Institute for Microelectronics (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
| | - Yikai Zheng
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Najam U Sakib
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Muhtasim Ul Karim Sadaf
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Rahul Pendurthi
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Riccardo Torsi
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
| | - Joshua A Robinson
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Physics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Tibor Grasser
- Institute for Microelectronics (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
| | - 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, Penn State University, University Park, Pennsylvania 16802, United States
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14
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Lau CS, Das S, Verzhbitskiy IA, Huang D, Zhang Y, Talha-Dean T, Fu W, Venkatakrishnarao D, Johnson Goh KE. Dielectrics for Two-Dimensional Transition-Metal Dichalcogenide Applications. ACS NANO 2023. [PMID: 37257134 DOI: 10.1021/acsnano.3c03455] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Despite over a decade of intense research efforts, the full potential of two-dimensional transition-metal dichalcogenides continues to be limited by major challenges. The lack of compatible and scalable dielectric materials and integration techniques restrict device performances and their commercial applications. Conventional dielectric integration techniques for bulk semiconductors are difficult to adapt for atomically thin two-dimensional materials. This review provides a brief introduction into various common and emerging dielectric synthesis and integration techniques and discusses their applicability for 2D transition metal dichalcogenides. Dielectric integration for various applications is reviewed in subsequent sections including nanoelectronics, optoelectronics, flexible electronics, valleytronics, biosensing, quantum information processing, and quantum sensing. For each application, we introduce basic device working principles, discuss the specific dielectric requirements, review current progress, present key challenges, and offer insights into future prospects and opportunities.
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Affiliation(s)
- Chit Siong Lau
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Sarthak Das
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Ivan A Verzhbitskiy
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Ding Huang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Yiyu Zhang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Teymour Talha-Dean
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
- Department of Physics and Astronomy, Queen Mary University of London, London E1 4NS, United Kingdom
| | - Wei Fu
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Dasari Venkatakrishnarao
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Kuan Eng Johnson Goh
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
- Department of Physics, National University of Singapore, 2 Science Drive 3, 117551, Singapore
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 50 Nanyang Avenue 639798, Singapore
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15
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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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16
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Su J, Li X, Xu M, Zhang J, Liu X, Zheng X, Shi Y, Zhang Q. Enhancing Photodetection Ability of MoS 2 Nanoscrolls via Interface Engineering. ACS APPLIED MATERIALS & INTERFACES 2023; 15:3307-3316. [PMID: 36596237 DOI: 10.1021/acsami.2c18537] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Van der Waals semiconductors have been really confirmed in two-dimensional (2D) layered systems beyond the traditional limits of lattice-matching requirements. The extension of this concept to the 1D atomic level may generate intriguing physical functionalities due to its non-covalent bonding surface. However, whether the curvature of the lattice in such rolled-up structures affects their optoelectronic features or the performance of devices established on them remains an open question. Here, MoS2-based nanoscrolls were obtained by virtue of an alkaline solution-assisted method and the 0D/1D (BaTiO3/MoS2) strategy to tune their optoelectronic properties and improve the light sensing performance was explored. The capillary force generated by a drop of NaHCO3 solution could drive the delamination of nanosheets from the underlying substrate and a spontaneous rolling-up process. The package of BaTiO3 particles in MoS2 nanoscrolls has been evident by TEM image, and the optical characterizations were mirrored via micro-Raman spectroscopy and photoluminescence. These bare MoS2 nanoscrolls reveal a reduced photoresponse compared to the plane structures due to the curvature of the lattice. However, such BaTiO3/MoS2 nanoscrolls exhibit a significantly improved photodetection (Rhybrid = 73.9 A/W vs Ronly = 1.1 A/W and R2D = 1.5 A/W at 470 nm, 0.58 mW·cm-2), potentially due to the carrier extraction/injection occurring between BaTiO3 and MoS2. This study thereby provides an insight into 1D van der Waals material community and demonstrates a general approach to fabricate high-performance 1D van der Waals optoelectronic devices.
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Affiliation(s)
- Jun Su
- Center for Advanced Optoelectronic Materials, College of Materials and Environmental Engineering, Hangzhou Dianzi University (HDU), Hangzhou 310018, China
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province, Hangzhou Dianzi University (HDU), Hangzhou 310018, P. R. China
| | - Xin Li
- Center for Advanced Optoelectronic Materials, College of Materials and Environmental Engineering, Hangzhou Dianzi University (HDU), Hangzhou 310018, China
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province, Hangzhou Dianzi University (HDU), Hangzhou 310018, P. R. China
| | - Minxuan Xu
- Center for Advanced Optoelectronic Materials, College of Materials and Environmental Engineering, Hangzhou Dianzi University (HDU), Hangzhou 310018, China
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province, Hangzhou Dianzi University (HDU), Hangzhou 310018, P. R. China
| | - Jian Zhang
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province, Hangzhou Dianzi University (HDU), Hangzhou 310018, P. R. China
| | - Xiaolian Liu
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province, Hangzhou Dianzi University (HDU), Hangzhou 310018, P. R. China
| | - Xin Zheng
- Center for Advanced Optoelectronic Materials, College of Materials and Environmental Engineering, Hangzhou Dianzi University (HDU), Hangzhou 310018, China
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province, Hangzhou Dianzi University (HDU), Hangzhou 310018, P. R. China
| | - Yueqin Shi
- Center for Advanced Optoelectronic Materials, College of Materials and Environmental Engineering, Hangzhou Dianzi University (HDU), Hangzhou 310018, China
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province, Hangzhou Dianzi University (HDU), Hangzhou 310018, P. R. China
| | - Qi Zhang
- Center for Advanced Optoelectronic Materials, College of Materials and Environmental Engineering, Hangzhou Dianzi University (HDU), Hangzhou 310018, China
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province, Hangzhou Dianzi University (HDU), Hangzhou 310018, P. R. China
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17
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Ravichandran H, Zheng Y, Schranghamer TF, Trainor N, Redwing JM, Das S. A Monolithic Stochastic Computing Architecture for Energy Efficient Arithmetic. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2206168. [PMID: 36308032 DOI: 10.1002/adma.202206168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/04/2022] [Indexed: 06/16/2023]
Abstract
As the energy and hardware investments necessary for conventional high-precision digital computing continue to explode in the era of artificial intelligence (AI), a change in paradigm that can trade precision for energy and resource efficiency is being sought for many computing applications. Stochastic computing (SC) is an attractive alternative since, unlike digital computers, which require many logic gates and a high transistor volume to perform basic arithmetic operations such as addition, subtraction, multiplication, sorting, etc., SC can implement the same using simple logic gates. While it is possible to accelerate SC using traditional silicon complementary metal-oxide-semiconductor (CMOS) technology, the need for extensive hardware investment to generate stochastic bits (s-bits), the fundamental computing primitive for SC, makes it less attractive. Memristor and spin-based devices offer natural randomness but depend on hybrid designs involving CMOS peripherals for accelerating SC, which increases area and energy burden. Here, the limitations of existing and emerging technologies are overcome, and a standalone SC architecture embedded in memory and based on 2D memtransistors is experimentally demonstrated. The monolithic and non-von-Neumann SC architecture occupies a small hardware footprint and consumes a miniscule amount of energy (<1 nJ) for both s-bit generation and arithmetic operations, highlighting the benefits of SC.
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Affiliation(s)
| | - Yikai Zheng
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Thomas F Schranghamer
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Nicholas Trainor
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- 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
- 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
- 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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18
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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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19
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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: 13] [Impact Index Per Article: 6.5] [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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