1
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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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2
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Yu H, Huang L, Zhou L, Peng Y, Li X, Yin P, Zhao J, Zhu M, Wang S, Liu J, Du H, Tang J, Zhang S, Zhou Y, Lu N, Liu K, Li N, Zhang G. Eight In. Wafer-Scale Epitaxial Monolayer MoS 2. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2402855. [PMID: 38683952 DOI: 10.1002/adma.202402855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 04/24/2024] [Indexed: 05/02/2024]
Abstract
Large-scale, high-quality, and uniform monolayer molybdenum disulfide (MoS2) films are crucial for their applications in next-generation electronics and optoelectronics. Epitaxy is a mainstream technique for achieving high-quality MoS2 films and is demonstrated at a wafer scale up to 4-in. In this study, the epitaxial growth of 8-in. wafer-scale highly oriented monolayer MoS2 on sapphire is reported as with excellent spatial homogeneity, using a specially designed vertical chemical vapor deposition (VCVD) system. Field effect transistors (FETs) based on the as-grown 8-in. wafer-scale monolayer MoS2 film are fabricated and exhibit high performances, with an average mobility and an on/off ratio of 53.5 cm2 V-1 s-1 and 107, respectively. In addition, batch fabrication of logic devices and 11-stage ring oscillators are also demonstrated, showcasing excellent electrical functions. This work may pave the way of MoS2 in practical industry-scale applications.
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Affiliation(s)
- Hua Yu
- Songshan Lake Materials Laboratory, Dongguan, 523808, China
| | - Liangfeng Huang
- Songshan Lake Materials Laboratory, Dongguan, 523808, China
- MOE Key Laboratory of Laser Life Science & Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, 510631, China
| | - Lanying Zhou
- Songshan Lake Materials Laboratory, Dongguan, 523808, China
| | - Yalin Peng
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Xiuzhen Li
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Peng Yin
- State Key Laboratory for Mesoscopic Physics, Frontiers Science Center for Nano-optoelectronics, School of Physics, Peking University, Beijing, 100190, China
- Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education), Department of Physics, Renmin University of China, Beijing, 100190, China
| | - Jiaojiao Zhao
- Songshan Lake Materials Laboratory, Dongguan, 523808, China
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Mingtong Zhu
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Shuopei Wang
- Songshan Lake Materials Laboratory, Dongguan, 523808, China
| | - Jieying Liu
- Songshan Lake Materials Laboratory, Dongguan, 523808, China
| | - Hongyue Du
- Songshan Lake Materials Laboratory, Dongguan, 523808, China
- MOE Key Laboratory of Laser Life Science & Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, 510631, China
| | - Jian Tang
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Songge Zhang
- Songshan Lake Materials Laboratory, Dongguan, 523808, China
| | - Yuchao Zhou
- Songshan Lake Materials Laboratory, Dongguan, 523808, China
| | - Nianpeng Lu
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Kaihui Liu
- Songshan Lake Materials Laboratory, Dongguan, 523808, China
- State Key Laboratory for Mesoscopic Physics, Frontiers Science Center for Nano-optoelectronics, School of Physics, Peking University, Beijing, 100190, China
| | - Na Li
- Songshan Lake Materials Laboratory, Dongguan, 523808, China
| | - Guangyu Zhang
- Songshan Lake Materials Laboratory, Dongguan, 523808, China
- Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
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3
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Li X, Wan J, Tang Y, Wang C, Zhang Y, Lv D, Guo M, Ma Y, Yang Y. Boosting the UV-vis-NIR Photodetection Performance of MoS 2 through the Cavity Enhancement Effect and Bulk Heterojunction Strategy. ACS APPLIED MATERIALS & INTERFACES 2024; 16:29003-29015. [PMID: 38788155 DOI: 10.1021/acsami.4c01823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Navigating more effective methods to enhance the photon utilization of photodetectors poses a significant challenge. This study initially investigates the impact of morphological alterations in 2H-MoS2 on photodetector (PD) performance. The results reveal that compared to layered MoS2 (MoS2 NLs), MoS2 nanotubes (MoS2 NTs) impart a cavity enhancement effect through multiple light reflections. This structural feature significantly enhances the photodetection performance of the MoS2-based PDs. We further employ the heterojunction strategy to construct Y-TiOPc NPs:MoS2 NTs, utilizing Y-TiOPc NPs (Y-type titanylphthalocyanine) as the vis-NIR photosensitizer and MoS2 NTs as the photon absorption enhancer. This approach not only addresses the weak absorption of MoS2 NTs in the near-infrared region but also enhances carrier generation, separation, and transport efficiency. Additionally, the band bending phenomenon induced by trapped-electrons at the interface between ITO and the photoactive layer significantly enhances the hole tunneling injection capability from the external circuit. By leveraging the synergistic effects of the aforementioned strategies, the PD based on Y-TiOPc NPs:MoS2 NTs (Y:MT-PD) exhibits superior photodetection performance in the wavelength range of 365-940 nm compared to MoS2 NLs-based PD and MoS2 NTs-based PD. Particularly noteworthy are the peak values of key metrics for Y:MT-PD, such as EQE, R, and D* that are 4947.6%, 20588 mA/W, and 1.94 × 1012 Jones, respectively. The multiperiod time-resolved photocurrent response curves of Y:MT-PD also surpass those of the other two PDs, displaying rapid, stable, and reproducible responses across all wavelengths. This study provides valuable insights for the further development of photoactive materials with a high photon utilization efficiency.
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Affiliation(s)
- Xiaolong Li
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Jundi Wan
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Yulu Tang
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Chenyu Wang
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Yahui Zhang
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Dongjun Lv
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, De Zhou University, Dezhou 253023, China
| | - Mingyuan Guo
- College of Chemistry and Materials Science, Weinan Normal University, Weinan 714099, China
| | - Yongning Ma
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Yuhao Yang
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Shaanxi University of Science and Technology, Xi'an 710021, China
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4
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Zhong G, Li JC. Multiple stochastic and inverse stochastic resonances with transition phenomena in complex corporate financial systems. CHAOS (WOODBURY, N.Y.) 2024; 34:063115. [PMID: 38838105 DOI: 10.1063/5.0198165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/15/2024] [Indexed: 06/07/2024]
Abstract
This study examines the role of periodic information, the mechanism of influence, stochastic resonance, and its controllable analysis in complex corporate financial systems. A stochastic predator-prey complex corporate financial system model driven by periodic information is proposed. Additionally, we introduce signal power amplification to quantify the stochastic resonance phenomenon and develop a method for analyzing stochastic resonance in financial predator-prey dynamics within complex corporate financial systems. We optimize a simplified integral calculation method to enhance the proposed model's performance, which demonstrates superiority over benchmark models based on empirical evidence. Based on stochastic simulations and numerical calculations, we can observe multiple stochastic and multiple inverse stochastic resonances. Furthermore, variations in initial financial information, periodic information frequency, and corporate growth capacity induced stochastic resonance and inverse stochastic resonance. These variations also led to state transitions between the two resonance behaviors, indicating transition phenomena. These findings suggest the potential for regulating and controlling stochastic and inverse stochastic resonance in complex corporate finance, enabling controllable stochastic resonance behaviors.
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Affiliation(s)
- Guangyan Zhong
- School of Finance, Yunnan University of Finance and Economics, Kunming 650221, People's Republic of China
| | - Jiang-Cheng Li
- School of Finance, Yunnan University of Finance and Economics, Kunming 650221, People's Republic of China
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5
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Liu Z, Qu K, Chen K, Li Z. Multi-Type Stochastic Resonances for Noise-Enhanced Mechanical, Optical, and Acoustic Sensing. RESEARCH (WASHINGTON, D.C.) 2024; 7:0386. [PMID: 38818382 PMCID: PMC11137332 DOI: 10.34133/research.0386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 04/21/2024] [Indexed: 06/01/2024]
Abstract
Stochastic resonance (SR) typically manifests in nonlinear systems, wherein the detection of a weak signal is bolstered by the addition of noise. Since its first discovery in a study of ice ages on Earth, various types of SRs have been observed in biological and physical systems and have been implemented in sensors to benefit from noise. However, a universally designed sensor architecture capable of accommodating different types of SRs has not been proposed, and the widespread applications of SRs in daily environments have not yet been demonstrated. Here, we propose a sensor architecture to simultaneously realize multi-type SRs and demonstrate their wide applications in mechanical, optical, and acoustic sensing domains. In particular, we find the coexistence of excitable SR and bistable SR in a sensor architecture composed of wirelessly coupled inductor-capacitor resonators connected to a nonlinearly saturable amplifier. In both types of SRs, adding noise to the system leads to a characteristic noise-enhanced signal-to-noise ratio (SNR). We further validate our findings through mechanical, optical, and acoustic sensing experiments and obtain noise-enhanced SNR by 9 dB, 3 dB, and 7 dB, respectively, compared to the standard methods devoid of SR integration. Our findings provide a general strategy to design various types of SRs and pave the way for the development of a distinctive class of sensors leveraging environmental noise, with potential applications ranging from biomedical devices to ambient sensing.
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Affiliation(s)
- Zhu Liu
- School of Physics and Electronics,
Hunan Normal University, Changsha 410081, China
- Key Laboratory of Physics and Devices in Post-Moore Er,
College of Hunan Province, Changsha 410081, China
| | - Kai Qu
- School of Electronic Science and Engineering,
Nanjing University, Nanjing 210023, China
| | - Ke Chen
- School of Electronic Science and Engineering,
Nanjing University, Nanjing 210023, China
| | - Zhipeng Li
- Department of Electrical and Computer Engineering,
National University of Singapore, Singapore 117583, Singapore
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6
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Syong WR, Fu JH, Kuo YH, Chu YC, Hakami M, Peng TY, Lynch J, Jariwala D, Tung V, Lu YJ. Enhanced Photogating Gain in Scalable MoS 2 Plasmonic Photodetectors via Resonant Plasmonic Metasurfaces. ACS NANO 2024. [PMID: 38315422 DOI: 10.1021/acsnano.3c10390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
Absorption of photons in atomically thin materials has become a challenge in the realization of ultrathin, high-performance optoelectronics. While numerous schemes have been used to enhance absorption in 2D semiconductors, such enhanced device performance in scalable monolayer photodetectors remains unattained. Here, we demonstrate wafer-scale integration of monolayer single-crystal MoS2 photodetectors with a nitride-based resonant plasmonic metasurface to achieve a high detectivity of 2.58 × 1012 Jones with a record-low dark current of 8 pA and long-term stability over 40 days. Upon comparison with control devices, we observe an overall enhancement factor of >100; this can be attributed to the local strong EM field enhanced photogating effect by the resonant plasmonic metasurface. Considering the compatibility of 2D semiconductors and hafnium nitride with the Si CMOS process and their scalability across wafer sizes, our results facilitate the smooth incorporation of 2D semiconductor-based photodetectors into the fields of imaging, sensing, and optical communication applications.
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Affiliation(s)
- Wei-Ren Syong
- Research Center for Applied Sciences, Academia Sinica, Taipei 11529, Taiwan
| | - Jui-Han Fu
- Department of Chemical System Engineering, School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
| | - Yu-Hsin Kuo
- Research Center for Applied Sciences, Academia Sinica, Taipei 11529, Taiwan
| | - Yu-Cheng Chu
- Research Center for Applied Sciences, Academia Sinica, Taipei 11529, Taiwan
- Department of Physics, National Taiwan University, Taipei 10617, Taiwan
| | - Mariam Hakami
- Department of Chemical System Engineering, School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
| | - Tzu-Yu Peng
- Research Center for Applied Sciences, Academia Sinica, Taipei 11529, Taiwan
- Department of Physics, National Taiwan University, Taipei 10617, Taiwan
| | - Jason Lynch
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Deep Jariwala
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Vincent Tung
- Department of Chemical System Engineering, School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
| | - Yu-Jung Lu
- Research Center for Applied Sciences, Academia Sinica, Taipei 11529, Taiwan
- Department of Physics, National Taiwan University, Taipei 10617, Taiwan
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7
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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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8
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Zhou J, Lan D, Zhang F, Cheng Y, Jia Z, Wu G, Yin P. Self-Assembled MoS 2 Cladding for Corrosion Resistant and Frequency-Modulated Electromagnetic Wave Absorption Materials from X-Band to Ku-Band. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2304932. [PMID: 37635102 DOI: 10.1002/smll.202304932] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/29/2023] [Indexed: 08/29/2023]
Abstract
Reasonable composition design and controllable structure are effective strategies for harmonic electromagnetic wave (EMW) adsorption of multi-component composites. On this basis, the hybrid MoS2 /CoS2 /VN multilayer structure with the triple heterogeneous interface is prepared by simple stirring hydrothermal, which can satisfy the synergistic interaction between different components and obtain excellent EMW absorption performance. Due to the presence of multiple heterogeneous interfaces, MoS2 /CoS2 /VN composites will produce strong interfacial polarization, while the defects in the sample will become the center of polarization, resulting in dipole polarization. Due to the excellent structural design of MoS2 /CoS2 /VN composite material, MoS2 /CoS2 /VN composite material not only has good conductive loss and polarization loss, but also can maintain excellent stability in simulated seawater, and enhance corrosion resistance. The MoS2 /CoS2 /VN composite with dual functions of corrosion resistant and microwave absorption achieves a minimum reflection loss (RL) of -50.48 dB and an effective absorption bandwidth of up to 5.76 GHz, covering both the X-band and Ku-band. Finally, this study provides a strong reference for the development of EMW absorption materials based on transition metal nitrides.
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Affiliation(s)
- Jixi Zhou
- College of Science, Sichuan Agricultural University, Ya'an, 625014, P. R. China
- Institute of Materials for Energy and Environment, State Key Laboratory of Bio-fibers and Eco-textiles, College of Materials Science and Engineering, Qingdao University, Qingdao, 442002, P. R. China
| | - Di Lan
- School of Materials Science and Engineering, Hubei University of Automotive Technology, Shiyan, 442002, P. R. China
| | - Feng Zhang
- Institute of Materials for Energy and Environment, State Key Laboratory of Bio-fibers and Eco-textiles, College of Materials Science and Engineering, Qingdao University, Qingdao, 442002, P. R. China
| | - Yuhang Cheng
- Institute of Materials for Energy and Environment, State Key Laboratory of Bio-fibers and Eco-textiles, College of Materials Science and Engineering, Qingdao University, Qingdao, 442002, P. R. China
| | - Zirui Jia
- College of Chemistry and Chemical Engineering, Qingdao University, Qingdao, 266071, P. R. China
| | - Guanglei Wu
- Institute of Materials for Energy and Environment, State Key Laboratory of Bio-fibers and Eco-textiles, College of Materials Science and Engineering, Qingdao University, Qingdao, 442002, P. R. China
- Key Laboratory of Engineering Dielectrics and Its Application, Ministry of Education, School of Electrical & Electronic Engineering, Harbin University of Science and Technology, Harbin, 150080, P. R. China
| | - Pengfei Yin
- College of Science, Sichuan Agricultural University, Ya'an, 625014, P. R. China
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9
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Liu B, Zheng X, Verma D, Zhao Y, Liang H, Li LJ, Chen J, Lai CS. Bi 2O 2Se-Based Bimode Noise Generator for the Application of Generative Adversarial Networks. ACS APPLIED MATERIALS & INTERFACES 2023; 15:49478-49486. [PMID: 37823797 DOI: 10.1021/acsami.3c10106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
In the emerging technology, the generative aversive networks (GANs), randomness, and unpredictability of inputting noises are the keys to the uniqueness, diversity, robustness, and security of the generated images. Compared with deterministic software-based noise generation, hardware-based noise generation introduces physical entropy sources, such as electronic and photonic noises, to add unpredictability. In this study, bimode Bi2O2Se-based noise generators have been demonstrated for the application of GANs. Harnessing its ultrahigh carrier mobility, excellent air stability, marvelous optoelectronic performance, as well as the unique surface resistive switching effect and defect locations in the energy diagram, Bi2O2Se provides a good material platform to easily integrate with multiple device architectures for generating noises in different physical sources. The noise of the black current mode in a photodetector architecture and the random telegraph noise in a memristor mode were measured, characterized, compared, and analyzed. A method of Markov chain equipped with K-means clustering was carried out to calculate the discrete noise states and the transition probability matrix between them. To evaluate the generated properties of the GANs based on the hardware noise source, the inception score and Fréchet inception distance were evaluated.
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Affiliation(s)
- Bo Liu
- Faculty of Information Technology, College of Microelectronics, Beijing University of Technology, Beijing 100124, People's Republic of China
| | - XingYi Zheng
- Department of Computer Science and Information Engineering, Chang Gung University, Guishan Dist., Taoyuan City 33302, Taiwan
| | - Dharmendra Verma
- Department of Electronic Engineering, Chang Gung University, Guishan Dist., Taoyuan 33302, Taiwan
| | - Yudi Zhao
- School of Information and Communication Engineering, Beijing Information Science & Technology University, Beijing 100101, China
| | - Hanyuan Liang
- School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, Pennsylvania 16801, United States
| | - Lain-Jong Li
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Kowloon, Hong Kong 999077, China
| | - Jenhui Chen
- Department of Computer Science and Information Engineering, Chang Gung University, Guishan Dist., Taoyuan City 33302, Taiwan
| | - Chao-Sung Lai
- Department of Electronic Engineering, Chang Gung University, Guishan Dist., Taoyuan 33302, Taiwan
- Artificial Intelligence and Green Technology Research Center, Chang Gung University, Guishan Dist., Taoyuan 33302,Taiwan
- Department of Nephrology, Chang Gung Memorial Hospital, Guishan Dist., Linkou 33305, Taiwan
- Department of Materials Engineering, Ming Chi University of Technology, Taishan Dist., New Taipei City 24301, Taiwan
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10
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Huh JH, Shiomi M, Miyagawa N. Control of stochastic and inverse stochastic resonances in a liquid-crystal electroconvection system using amplitude and phase noises. Sci Rep 2023; 13:16883. [PMID: 37803168 PMCID: PMC10558573 DOI: 10.1038/s41598-023-44043-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 10/03/2023] [Indexed: 10/08/2023] Open
Abstract
Stochastic and inverse stochastic resonances are counterintuitive phenomena, where noise plays a pivotal role in the dynamics of various biological and engineering systems. Even though these resonances have been identified in various systems, a transition between them has never been observed before. The present study demonstrates the presence of both resonances in a liquid crystal electroconvection system using combined amplitude and phase noises, which correspond to colored noises with appropriate cutoff frequencies (i.e., finite correlation times). We established the emergence of both resonances and their transition through systematic control of the electroconvection threshold voltage using these two noise sources. Our numerical simulations were experimentally confirmed and revealed how the output performance of the system could be controlled by combining the intensity and cutoff frequency of the two noises. Furthermore, we suggested the crucial contribution of a usually overlooked additional phase noise to the advancements in various noise-related fields.
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Affiliation(s)
- Jong-Hoon Huh
- Department of Physics and Information Technology, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka, 820-8502, Japan.
| | - Masato Shiomi
- Department of Physics and Information Technology, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka, 820-8502, Japan
| | - Naoto Miyagawa
- Department of Physics and Information Technology, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka, 820-8502, Japan
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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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Zhu D, Qiao M, Yan J, Xie J, Guo H, Deng S, He G, Zhao Y, Luo M. Three-dimensional patterning of MoS 2 with ultrafast laser. NANOSCALE 2023; 15:14837-14846. [PMID: 37646207 DOI: 10.1039/d3nr01669b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Transition metal chalcogenides, a special two-dimensional (2D) material emerged in recent years, possess unique optoelectronic properties and have been used to fabricate various optoelectronic devices. While it is essential to manufacture multifunctional devices with complex nanostructures for practical applications, 2D material devices present a tendency toward miniaturization. However, the controllable fabrication of complex nanostructures on 2D materials remains a challenge. Herein, we propose a method to create designed three-dimensional (3D) patterns on the MoS2 surface by modulating the interaction between an ultrafast laser and MoS2. Three different nanostructures, including flat, bulge, and craters, can be fabricated through laser-induced surface morphology transformation, which is related to thermal diffusion, oxidation, and ablation processes. The MoS2 field effect transistor is fabricated by ultrafast laser excitation which exhibits enhanced electrical properties. This study provides a promising strategy for 3D pattern fabrication, which is helpful for the development of multifunctional microdevices.
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Affiliation(s)
- Dezhi Zhu
- State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
| | - Ming Qiao
- State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
| | - Jianfeng Yan
- State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
| | - Jiawang Xie
- State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
| | - Heng Guo
- State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
| | - Shengfa Deng
- State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
| | - Guangzhi He
- State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
| | - Yuzhi Zhao
- State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
| | - Ma Luo
- State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
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13
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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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14
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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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15
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Li Z, Li C, Xiong Z, Xu G, Wang YR, Tian X, Yang X, Liu Z, Zeng Q, Lin R, Li Y, Lee JKW, Ho JS, Qiu CW. Stochastic Exceptional Points for Noise-Assisted Sensing. PHYSICAL REVIEW LETTERS 2023; 130:227201. [PMID: 37327430 DOI: 10.1103/physrevlett.130.227201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 04/07/2023] [Indexed: 06/18/2023]
Abstract
Noise is a fundamental challenge for sensors deployed in daily environments for ambient sensing, health monitoring, and wireless networking. Current strategies for noise mitigation rely primarily on reducing or removing noise. Here, we introduce stochastic exceptional points and show the utility to reverse the detrimental effect of noise. The stochastic process theory illustrates that the stochastic exceptional points manifest as fluctuating sensory thresholds that give rise to stochastic resonance, a counterintuitive phenomenon in which the added noise increases the system's ability to detect weak signals. Demonstrations using a wearable wireless sensor show that the stochastic exceptional points lead to more accurate tracking of a person's vital signs during exercise. Our results may lead to a distinct class of sensors that overcome and are enhanced by ambient noise for applications ranging from healthcare to the internet of things.
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Affiliation(s)
- Zhipeng Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Chenhui Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Ze Xiong
- Wireless and Smart Bioelectronics Lab, School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Guoqiang Xu
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Yongtai Raymond Wang
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117593, Singapore
| | - Xi Tian
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Xin Yang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Zhu Liu
- School of Physics and Electronics, Hunan Normal University, Changsha, Hunan 410081, China
| | - Qihang Zeng
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Rongzhou Lin
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Ying Li
- Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China
- International Joint Innovation Center, Key Lab of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang, The Electromagnetics Academy of Zhejiang University, Zhejiang University, Haining 314400, China
| | - Jason Kai Wei Lee
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117593, Singapore
- Heat Resilience and Performance Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
- Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - John S Ho
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Cheng-Wei Qiu
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
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16
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Wali A, Ravichandran H, Das S. Hardware Trojans based on two-dimensional memtransistors. NANOSCALE HORIZONS 2023; 8:603-615. [PMID: 37021644 DOI: 10.1039/d2nh00568a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Hardware Trojans (HTs) have emerged as a major security threat for integrated circuits (ICs) owing to the involvement of untrustworthy actors in the globally distributed semiconductor supply chain. HTs are intentional malicious modifications, which remain undetectable through simple electrical measurements but can cause catastrophic failure in the functioning of ICs in mission critical applications. In this article, we show how two-dimensional (2D) material based in-memory computing elements such as memtransistors can be used as hardware Trojans. We found that logic gates based on 2D memtransistors can be made to malfunction by exploiting their inherent programming capabilities. While we use 2D memtransistor-based ICs as the testbed for our demonstration, the results are equally applicable to any state-of-the-art and emerging in-memory computing technologies.
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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
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17
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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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18
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Schranghamer TF, Sakib NU, Sadaf MUK, Subbulakshmi Radhakrishnan S, Pendurthi R, Agyapong AD, Stepanoff SP, Torsi R, Chen C, Redwing JM, Robinson JA, Wolfe DE, Mohney SE, Das S. Ultrascaled Contacts to Monolayer MoS 2 Field Effect Transistors. NANO LETTERS 2023; 23:3426-3434. [PMID: 37058411 DOI: 10.1021/acs.nanolett.3c00466] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Two-dimensional (2D) semiconductors possess promise for the development of field-effect transistors (FETs) at the ultimate scaling limit due to their strong gate electrostatics. However, proper FET scaling requires reduction of both channel length (LCH) and contact length (LC), the latter of which has remained a challenge due to increased current crowding at the nanoscale. Here, we investigate Au contacts to monolayer MoS2 FETs with LCH down to 100 nm and LC down to 20 nm to evaluate the impact of contact scaling on FET performance. Au contacts are found to display a ∼2.5× reduction in the ON-current, from 519 to 206 μA/μm, when LC is scaled from 300 to 20 nm. It is our belief that this study is warranted to ensure an accurate representation of contact effects at and beyond the technology nodes currently occupied by silicon.
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Affiliation(s)
- Thomas F Schranghamer
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Najam U Sakib
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Muhtasim Ul Karim Sadaf
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Shiva Subbulakshmi Radhakrishnan
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Rahul Pendurthi
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Ama Duffie Agyapong
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Sergei P Stepanoff
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Applied Research Laboratory, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Riccardo Torsi
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Chen Chen
- 2D Crystal Consortium Materials Innovation Platform, Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Joan M Redwing
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- 2D Crystal Consortium Materials Innovation Platform, Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Electrical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Joshua A Robinson
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Douglas E Wolfe
- 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
- Applied Research Laboratory, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Suzanne E Mohney
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Electrical Engineering, 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
- Department of Electrical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
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19
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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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20
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Jayachandran D, Pannone A, Das M, Schranghamer TF, Sen D, Das S. Insect-Inspired, Spike-Based, in-Sensor, and Night-Time Collision Detector Based on Atomically Thin and Light-Sensitive Memtransistors. ACS NANO 2022; 17:1068-1080. [PMID: 36584350 DOI: 10.1021/acsnano.2c07877] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Detecting a potential collision at night is a challenging task owing to the lack of discernible features that can be extracted from the available visual stimuli. To alert the driver or, alternatively, the maneuvering system of an autonomous vehicle, current technologies utilize resource draining and expensive solutions such as light detection and ranging (LiDAR) or image sensors coupled with extensive software running sophisticated algorithms. In contrast, insects perform the same task of collision detection with frugal neural resources. Even though the general architecture of separate sensing and processing modules is the same in insects and in image-sensor-based collision detectors, task-specific obstacle avoidance algorithms allow insects to reap substantial benefits in terms of size and energy. Here, we show that insect-inspired collision detection algorithms, when implemented in conjunction with in-sensor processing and enabled by innovative optoelectronic integrated circuits based on atomically thin and photosensitive memtransistor technology, can greatly simplify collision detection at night. The proposed collision detector eliminates the need for image capture and image processing yet demonstrates timely escape responses for cars on collision courses under various real-life scenarios at night. The collision detector also has a small footprint of ∼40 μm2 and consumes only a few hundred picojoules of energy. We strongly believe that the proposed collision detectors can augment existing sensors necessary for ensuring autonomous vehicular safety.
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Affiliation(s)
- Darsith Jayachandran
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania16802, United States
| | - Andrew Pannone
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania16802, United States
| | - Mayukh Das
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania16802, United States
| | - Thomas F Schranghamer
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania16802, United States
| | - Dipanjan Sen
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania16802, United States
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania16802, United States
- Electrical Engineering and Computer Science, Penn State University, University Park, Pennsylvania16802, United States
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania16802, United States
- Materials Research Institute, Penn State University, University Park, Pennsylvania16802, United States
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21
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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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22
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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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23
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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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24
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Subbulakshmi Radhakrishnan S, Chakrabarti S, Sen D, Das M, Schranghamer TF, Sebastian A, Das S. A Sparse and Spike-Timing-Based Adaptive Photoencoder for Augmenting Machine Vision for Spiking Neural Networks. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2202535. [PMID: 35674268 DOI: 10.1002/adma.202202535] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 05/31/2022] [Indexed: 06/15/2023]
Abstract
The representation of external stimuli in the form of action potentials or spikes constitutes the basis of energy efficient neural computation that emerging spiking neural networks (SNNs) aspire to imitate. With recent evidence suggesting that information in the brain is more often represented by explicit firing times of the neurons rather than mean firing rates, it is imperative to develop novel hardware that can accelerate sparse and spike-timing-based encoding. Here a medium-scale integrated circuit composed of two cascaded three-stage inverters and one XOR logic gate fabricated using a total of 21 memtransistors based on photosensitive 2D monolayer MoS2 for spike-timing-based encoding of visual information, is introduced. It is shown that different illumination intensities can be encoded into sparse spiking with time-to-first-spike representing the illumination information, that is, higher intensities invoke earlier spikes and vice versa. In addition, non-volatile and analog programmability in the photoencoder is exploited for adaptive photoencoding that allows expedited spiking under scotopic (low-light) and deferred spiking under photopic (bright-light) conditions, respectively. Finally, low energy expenditure of less than 1 µJ by the 2D-memtransistor-based photoencoder highlights the benefits of in-sensor and bioinspired design that can be transformative for the acceleration of SNNs.
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Affiliation(s)
| | - Shakya Chakrabarti
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, 16802, USA
| | - Dipanjan Sen
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Mayukh Das
- 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
| | - Amritanand Sebastian
- 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 and Computer Science, 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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25
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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: 33] [Impact Index Per Article: 16.5] [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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26
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Dodda A, Jayachandran D, Pannone A, Trainor N, Stepanoff SP, Steves MA, Radhakrishnan SS, Bachu S, Ordonez CW, Shallenberger JR, Redwing JM, Knappenberger KL, Wolfe DE, Das S. Active pixel sensor matrix based on monolayer MoS 2 phototransistor array. NATURE MATERIALS 2022; 21:1379-1387. [PMID: 36396961 DOI: 10.1038/s41563-022-01398-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
In-sensor processing, which can reduce the energy and hardware burden for many machine vision applications, is currently lacking in state-of-the-art active pixel sensor (APS) technology. Photosensitive and semiconducting two-dimensional (2D) materials can bridge this technology gap by integrating image capture (sense) and image processing (compute) capabilities in a single device. Here, we introduce a 2D APS technology based on a monolayer MoS2 phototransistor array, where each pixel uses a single programmable phototransistor, leading to a substantial reduction in footprint (900 pixels in ∼0.09 cm2) and energy consumption (100s of fJ per pixel). By exploiting gate-tunable persistent photoconductivity, we achieve a responsivity of ∼3.6 × 107 A W-1, specific detectivity of ∼5.6 × 1013 Jones, spectral uniformity, a high dynamic range of ∼80 dB and in-sensor de-noising capabilities. Further, we demonstrate near-ideal yield and uniformity in photoresponse across the 2D APS array.
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Affiliation(s)
- Akhil Dodda
- Engineering Science and Mechanics, Penn State University, University Park, PA, USA
| | - Darsith Jayachandran
- Engineering Science and Mechanics, Penn State University, University Park, PA, USA
| | - Andrew Pannone
- Engineering Science and Mechanics, Penn State University, University Park, PA, USA
| | - Nicholas Trainor
- Materials Science and Engineering, Penn State University, University Park, PA, USA
- Materials Research Institute, Penn State University, University Park, PA, USA
| | - Sergei P Stepanoff
- Materials Science and Engineering, Penn State University, University Park, PA, USA
| | - Megan A Steves
- Department of Chemistry, Penn State University, University Park, PA, USA
| | | | - Saiphaneendra Bachu
- Materials Science and Engineering, Penn State University, University Park, PA, USA
| | - Claudio W Ordonez
- Department of Chemistry, Penn State University, University Park, PA, USA
| | | | - Joan M Redwing
- Materials Science and Engineering, Penn State University, University Park, PA, USA
- Materials Research Institute, Penn State University, University Park, PA, USA
| | | | - Douglas E Wolfe
- Engineering Science and Mechanics, Penn State University, University Park, PA, USA
- Materials Science and Engineering, Penn State University, University Park, PA, USA
- Applied Research Laboratory, 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.
- Applied Research Laboratory, Penn State University, University Park, PA, USA.
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, USA.
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27
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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: 15] [Impact Index Per Article: 7.5] [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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28
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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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29
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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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30
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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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31
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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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Lin H, Jiang A, Xing S, Li L, Cheng W, Li J, Miao W, Zhou X, Tian L. Advances in Self-Powered Ultraviolet Photodetectors Based on P-N Heterojunction Low-Dimensional Nanostructures. NANOMATERIALS 2022; 12:nano12060910. [PMID: 35335723 PMCID: PMC8953703 DOI: 10.3390/nano12060910] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/02/2022] [Accepted: 03/03/2022] [Indexed: 02/04/2023]
Abstract
Self-powered ultraviolet (UV) photodetectors have attracted considerable attention in recent years because of their vast applications in the military and civil fields. Among them, self-powered UV photodetectors based on p-n heterojunction low-dimensional nanostructures are a very attractive research field due to combining the advantages of low-dimensional semiconductor nanostructures (such as large specific surface area, excellent carrier transmission channel, and larger photoconductive gain) with the feature of working independently without an external power source. In this review, a selection of recent developments focused on improving the performance of self-powered UV photodetectors based on p-n heterojunction low-dimensional nanostructures from different aspects are summarized. It is expected that more novel, dexterous, and intelligent photodetectors will be developed as soon as possible on the basis of these works.
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Affiliation(s)
- Haowei Lin
- School of Materials Science and Engineering, Henan University of Technology, Zhengzhou 450001, China; (A.J.); (S.X.); (L.L.); (W.C.); (J.L.); (W.M.); (X.Z.); (L.T.)
- Henan International Joint Laboratory of Nano-Photoelectric Magnetic Materials, Henan University of Technology, Zhengzhou 450001, China
- Correspondence:
| | - Ao Jiang
- School of Materials Science and Engineering, Henan University of Technology, Zhengzhou 450001, China; (A.J.); (S.X.); (L.L.); (W.C.); (J.L.); (W.M.); (X.Z.); (L.T.)
| | - Shibo Xing
- School of Materials Science and Engineering, Henan University of Technology, Zhengzhou 450001, China; (A.J.); (S.X.); (L.L.); (W.C.); (J.L.); (W.M.); (X.Z.); (L.T.)
| | - Lun Li
- School of Materials Science and Engineering, Henan University of Technology, Zhengzhou 450001, China; (A.J.); (S.X.); (L.L.); (W.C.); (J.L.); (W.M.); (X.Z.); (L.T.)
| | - Wenxi Cheng
- School of Materials Science and Engineering, Henan University of Technology, Zhengzhou 450001, China; (A.J.); (S.X.); (L.L.); (W.C.); (J.L.); (W.M.); (X.Z.); (L.T.)
| | - Jinling Li
- School of Materials Science and Engineering, Henan University of Technology, Zhengzhou 450001, China; (A.J.); (S.X.); (L.L.); (W.C.); (J.L.); (W.M.); (X.Z.); (L.T.)
| | - Wei Miao
- School of Materials Science and Engineering, Henan University of Technology, Zhengzhou 450001, China; (A.J.); (S.X.); (L.L.); (W.C.); (J.L.); (W.M.); (X.Z.); (L.T.)
| | - Xuefei Zhou
- School of Materials Science and Engineering, Henan University of Technology, Zhengzhou 450001, China; (A.J.); (S.X.); (L.L.); (W.C.); (J.L.); (W.M.); (X.Z.); (L.T.)
| | - Li Tian
- School of Materials Science and Engineering, Henan University of Technology, Zhengzhou 450001, China; (A.J.); (S.X.); (L.L.); (W.C.); (J.L.); (W.M.); (X.Z.); (L.T.)
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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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Liao Z, Wang Z, Yamahara H, Tabata H. Low-power-consumption physical reservoir computing model based on overdamped bistable stochastic resonance system. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.09.074] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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35
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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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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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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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38
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Roy PK, Marvan P, Mazánek V, Antonatos N, Bouša D, Kovalska E, Sedmidubský D, Sofer Z. Self-Powered Broadband Photodetector and Sensor Based on Novel Few-Layered Pd 3(PS 4) 2 Nanosheets. ACS APPLIED MATERIALS & INTERFACES 2021; 13:30806-30817. [PMID: 34161061 DOI: 10.1021/acsami.1c05974] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Optoelectronics and sensing devices are of enormous importance in our modern lives, which has propelled the scientific community to explore new two-dimensional (2D) nanomaterials to meet the requirements of future devices. Herein, we present the exfoliation of palladium thiophosphate (Pd3(PS4)2) by mechanical shear force exfoliation. The Pd3(PS4)2-based photoelectrochemical (PEC) device demonstrated self-powered broadband photodetection in the range of 385-940 nm with an unprecedented responsivity of 2 A W-1 and a specific detectivity of about 8.67 × 1011 cm Hz1/2 W-1 under the illumination of 420 nm LED light. The crucial parameters such as photoresponsivity, response, and recovery time of the device can be controlled by an externally applied voltage and the analyte concentration. Moreover, Pd3(PS4)2-based vapor-sensing devices exhibited frequency-dependent selective acetone sensing in the presence of other organic vapors with an ultrafast response and a recovery time of less than 1 s. Finally, the photocatalytic activity of Pd3(PS4)2 was revealed, which can be attributed to the presence of an appropriate band alignment with the catalytic activity of Pd. This novel material with the aforementioned fascinating phenomenon will pave the way toward practical future applications in optoelectronics and sensing.
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Affiliation(s)
- Pradip Kumar Roy
- Department of Inorganic Chemistry, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - Petr Marvan
- Department of Inorganic Chemistry, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - Vlastimil Mazánek
- Department of Inorganic Chemistry, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - Nikolas Antonatos
- Department of Inorganic Chemistry, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - Daniel Bouša
- Department of Inorganic Chemistry, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - Evgeniya Kovalska
- Department of Inorganic Chemistry, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - David Sedmidubský
- Department of Inorganic Chemistry, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - Zdeněk Sofer
- Department of Inorganic Chemistry, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague 6, Czech Republic
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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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40
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Zhu J. Unified mechanism of inverse stochastic resonance for monostability and bistability in Hindmarsh-Rose neuron. CHAOS (WOODBURY, N.Y.) 2021; 31:033119. [PMID: 33810765 DOI: 10.1063/5.0041410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 02/16/2021] [Indexed: 06/12/2023]
Abstract
Noise is ubiquitous and has been verified to play constructive roles in various systems, among which the inverse stochastic resonance (ISR) has aroused much attention in contrast to positive effects such as stochastic resonance. The ISR has been observed in both bistable and monostable systems for which the mechanisms are revealed as noise-induced biased switching and noise-enhanced stability, respectively. In this paper, we investigate the ISR phenomenon in the monostable and bistable Hindmarsh-Rose neurons within a unified framework of large deviation theory. The critical noise strengths for both cases can be obtained by matching the timescales between noise-induced boundary crossing and the limit cycle. Furthermore, different stages of ISR are revealed by the bursting frequency distribution, where the gradual increase of the peak bursting frequency can also be explained within the same framework. The perspective and results in this paper may shed some light on the understanding of the noise-induced complex phenomena in stochastic dynamical systems.
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Affiliation(s)
- Jinjie Zhu
- School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
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41
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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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42
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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: 137] [Impact Index Per Article: 45.7] [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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43
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Nasr JR, Simonson N, Oberoi A, Horn MW, Robinson JA, Das S. Low-Power and Ultra-Thin MoS 2 Photodetectors on Glass. ACS NANO 2020; 14:15440-15449. [PMID: 33112615 DOI: 10.1021/acsnano.0c06064] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Integration of low-power consumer electronics on glass can revolutionize the automotive and transport sectors, packaging industry, smart building and interior design, healthcare, life science engineering, display technologies, and many other applications. However, direct growth of high-performance, scalable, and reliable electronic materials on glass is difficult owing to low thermal budget. Similarly, development of energy-efficient electronic and optoelectronic devices on glass requires manufacturing innovations. Here, we accomplish both by relatively low-temperature (<600 °C) metal-organic chemical vapor deposition growth of atomically thin MoS2 on multicomponent glass and fabrication of low-power phototransistors using atomic layer deposition (ALD)-grown, high-k, and ultra-thin (∼20 nm) Al2O3 as the top-gate dielectric, circumventing the challenges associated with the ALD nucleation of oxides on inert basal planes of van der Waals materials. The MoS2 photodetectors demonstrate the ability to detect low-intensity visible light at high speed and low energy expenditure of ∼100 pico Joules. Furthermore, low device-to-device performance variation across the entire 1 cm2 substrate and aggressive channel length scalability confirm the technology readiness level of ultra-thin MoS2 photodetectors on glass.
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Affiliation(s)
- Joseph R Nasr
- Deparment of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Nicholas Simonson
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Aaryan Oberoi
- Deparment of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Mark W Horn
- Deparment of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Joshua A Robinson
- 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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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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