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Sakib NU, Karim Sadaf MU, Pannone A, Ghosh S, Zheng Y, Ravichandran H, Das S. A Crayfish-Inspired Sensor Fusion Platform for Super Additive Integration of Visual, Chemical, and Tactile Information. NANO LETTERS 2024; 24:6948-6956. [PMID: 38810209 DOI: 10.1021/acs.nanolett.4c01187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
The concept of cross-sensor modulation, wherein one sensor modality can influence another's response, is often overlooked in traditional sensor fusion architectures, leading to missed opportunities for enhancing data accuracy and robustness. In contrast, biological systems, such as aquatic animals like crayfish, demonstrate superior sensor fusion through multisensory integration. These organisms adeptly integrate visual, tactile, and chemical cues to perform tasks such as evading predators and locating prey. Drawing inspiration from this, we propose a neuromorphic platform that integrates graphene-based chemitransistors, monolayer molybdenum disulfide (MoS2) based photosensitive memtransistors, and triboelectric tactile sensors to achieve "Super-Additive" responses to weak chemical, visual, and tactile cues and demonstrate contextual response modulation, also referred to as the "Inverse Effectiveness Effect." We hold the view that integrating bio-inspired sensor fusion principles across various modalities holds promise for a wide range of applications.
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Affiliation(s)
- Najam U Sakib
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Muhtasim Ul Karim Sadaf
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Andrew Pannone
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Subir Ghosh
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Yikai Zheng
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Harikrishnan Ravichandran
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
- Electrical Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Materials Research Institute, Penn State University, University Park, Pennsylvania 16802, United States
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2
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Tan T, Guo H, Li Y, Wang Y, Cai W, Bao W, Zhou P, Feng X. Integration of MoS 2 Memtransistor Devices and Analogue Circuits for Sensor Fusion in Autonomous Vehicle Target Localization. ACS NANO 2024; 18:13652-13661. [PMID: 38751043 DOI: 10.1021/acsnano.4c00456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
In contemporary autonomous driving systems relying on sensor fusion, traditional digital processors encounter challenges associated with analogue-to-digital conversion and iterative vector-matrix operations, which are encumbered by limitations in terms of response time and energy consumption. In this study, we present an analogue Kalman filter circuit based on molybdenum disulfide (MoS2) memtransistor, designed to accelerate sensor fusion for precise localization in autonomous vehicle applications. The nonvolatile memory characteristics of the memtransistor allow for the storage of a fixed Kalman gain, which eliminates the data convergence and thus accelerates the processing speeds. Additionally, the modulation of multiple conductance states by the gate terminal enables fast adaptability to diverse autonomous driving scenarios by tuning multiple Kalman filter gains. Our proposed analogue Kalman filter circuit accurately estimates the position coordinates of target vehicles by fusing sensor data from light detection and ranging (LiDAR), millimeter-wave radar (Radar), and camera, and it successfully solves real-word problems in a signal-free crossroad intersection. Notably, our system achieves a 1000-fold improvement in energy efficiency compared to that of digital circuits. This work underscores the viability of a memtransistor for achieving fast, energy-efficient real-time sensing, and continuous signal processing in advanced sensor fusion technology.
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Affiliation(s)
- Tian Tan
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Haoyue Guo
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yida Li
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yafei Wang
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weiwei Cai
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Wenzhong Bao
- School of Microelectronics, Fudan University, Shanghai 200433, China
- Shaoxing Laboratory, Shaoxing 312300, China
| | - Peng Zhou
- School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Xuewei Feng
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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3
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Wali A, Ravichandran H, Das S. A 2D Cryptographic Hash Function Incorporating Homomorphic Encryption for Secure Digital Signatures. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2400661. [PMID: 38373292 DOI: 10.1002/adma.202400661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Indexed: 02/21/2024]
Abstract
User authentication is a critical aspect of any information exchange system which verifies the identities of individuals seeking access to sensitive information. Conventionally, this approachrelies on establishing robust digital signature protocols which employ asymmetric encryption techniques involving a key pair consisting of a public key and its matching private key. In this article, a user verification platform constructed using integrated circuits (ICs) with atomically thin two-dimensional (2D) monolayer molybdenum disulfide (MoS2 ) memtransistors is presented. First, generation of secure cryptographic keys is demonstrated by exploiting the inherent stochasticity of carrier trapping and detrapping at the 2D/oxide interface trap sites. Subsequently, the ability to manipulate the functionality of logical NOR is leveraged to create a secure one-way hash function which when homomorphically operated upon with NAND, XOR, OR, NOT, and AND logic circuits generate distinct digital signatures. These signatures when subsequently decrypted, verify the authenticity of the receiver while ensuring complete preservation of data integrity and confidentiality as the underlying information is never revealed. Finally, the advantages of implementing a NOR-based hashing techniques in comparison to the conventional XOR-based encryption method are established. This demonstration highlights the potential of 2D-based ICs in developing critical hardware information security primitives.
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Affiliation(s)
- Akshay Wali
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, 16802, USA
| | | | - Saptarshi Das
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, 16802, USA
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- Materials Research Institute, Penn State University, University Park, PA, 16802, USA
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Zhou H, Li S, Ang KW, Zhang YW. Recent Advances in In-Memory Computing: Exploring Memristor and Memtransistor Arrays with 2D Materials. NANO-MICRO LETTERS 2024; 16:121. [PMID: 38372805 PMCID: PMC10876512 DOI: 10.1007/s40820-024-01335-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/25/2023] [Indexed: 02/20/2024]
Abstract
The conventional computing architecture faces substantial challenges, including high latency and energy consumption between memory and processing units. In response, in-memory computing has emerged as a promising alternative architecture, enabling computing operations within memory arrays to overcome these limitations. Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays, rapid response times, and ability to emulate biological synapses. Among these devices, two-dimensional (2D) material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing, thanks to their exceptional performance driven by the unique properties of 2D materials, such as layered structures, mechanical flexibility, and the capability to form heterojunctions. This review delves into the state-of-the-art research on 2D material-based memristive arrays, encompassing critical aspects such as material selection, device performance metrics, array structures, and potential applications. Furthermore, it provides a comprehensive overview of the current challenges and limitations associated with these arrays, along with potential solutions. The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing, leveraging the potential of 2D material-based memristive devices.
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Affiliation(s)
- Hangbo Zhou
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Republic of Singapore
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Republic of Singapore.
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore, 138634, Republic of Singapore.
| | - Yong-Wei Zhang
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore.
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Liu A, Zhang X, Liu Z, Li Y, Peng X, Li X, Qin Y, Hu C, Qiu Y, Jiang H, Wang Y, Li Y, Tang J, Liu J, Guo H, Deng T, Peng S, Tian H, Ren TL. The Roadmap of 2D Materials and Devices Toward Chips. NANO-MICRO LETTERS 2024; 16:119. [PMID: 38363512 PMCID: PMC10873265 DOI: 10.1007/s40820-023-01273-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/30/2023] [Indexed: 02/17/2024]
Abstract
Due to the constraints imposed by physical effects and performance degradation, silicon-based chip technology is facing certain limitations in sustaining the advancement of Moore's law. Two-dimensional (2D) materials have emerged as highly promising candidates for the post-Moore era, offering significant potential in domains such as integrated circuits and next-generation computing. Here, in this review, the progress of 2D semiconductors in process engineering and various electronic applications are summarized. A careful introduction of material synthesis, transistor engineering focused on device configuration, dielectric engineering, contact engineering, and material integration are given first. Then 2D transistors for certain electronic applications including digital and analog circuits, heterogeneous integration chips, and sensing circuits are discussed. Moreover, several promising applications (artificial intelligence chips and quantum chips) based on specific mechanism devices are introduced. Finally, the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed, and potential development pathways or roadmaps are further speculated and outlooked.
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Affiliation(s)
- Anhan Liu
- School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100049, People's Republic of China
| | - Xiaowei Zhang
- School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100049, People's Republic of China
| | - Ziyu Liu
- School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Yuning Li
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, People's Republic of China
| | - Xueyang Peng
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, People's Republic of China
- School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Xin Li
- State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan, 030051, People's Republic of China
| | - Yue Qin
- State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan, 030051, People's Republic of China
| | - Chen Hu
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, People's Republic of China
- School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Yanqing Qiu
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, People's Republic of China
- School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Han Jiang
- School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Yang Wang
- School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Yifan Li
- School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100049, People's Republic of China
| | - Jun Tang
- State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan, 030051, People's Republic of China
| | - Jun Liu
- State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan, 030051, People's Republic of China
| | - Hao Guo
- State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan, 030051, People's Republic of China.
| | - Tao Deng
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, People's Republic of China.
| | - Songang Peng
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, People's Republic of China.
- IMECAS-HKUST-Joint Laboratory of Microelectronics, Beijing, 100029, People's Republic of China.
| | - He Tian
- School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100049, People's Republic of China.
| | - Tian-Ling Ren
- School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100049, People's Republic of China.
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Zheng Y, Ghosh S, Das S. A Butterfly-Inspired Multisensory Neuromorphic Platform for Integration of Visual and Chemical Cues. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023:e2307380. [PMID: 38069632 DOI: 10.1002/adma.202307380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 11/25/2023] [Indexed: 12/23/2023]
Abstract
Unisensory cues are often insufficient for animals to effectively engage in foraging, mating, and predatory activities. In contrast, integration of cues collected from multiple sensory organs enhances the overall perceptual experience and thereby facilitates better decision-making. Despite the importance of multisensory integration in animals, the field of artificial intelligence (AI) and neuromorphic computing has primarily focused on processing unisensory information. This lack of emphasis on multisensory integration can be attributed to the absence of a miniaturized hardware platform capable of co-locating multiple sensing modalities and enabling in-sensor and near-sensor processing. In this study, this limitation is addressed by utilizing the chemo-sensing properties of graphene and the photo-sensing capability of monolayer molybdenum disulfide (MoS2 ) to create a multisensory platform for visuochemical integration. Additionally, the in-memory-compute capability of MoS2 memtransistors is leveraged to develop neural circuits that facilitate multisensory decision-making. The visuochemical integration platform is inspired by intricate courtship of Heliconius butterflies, where female species rely on the integration of visual cues (such as wing color) and chemical cues (such as pheromones) generated by the male butterflies for mate selection. The butterfly-inspired visuochemical integration platform has significant implications in both robotics and the advancement of neuromorphic computing, going beyond unisensory intelligence and information processing.
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Affiliation(s)
- Yikai Zheng
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Subir Ghosh
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
- Electrical Engineering, Penn State University, University Park, PA, 16802, USA
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- Materials Research Institute, Penn State University, University Park, PA, 16802, USA
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7
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Bonnet D, Hirtzlin T, Majumdar A, Dalgaty T, Esmanhotto E, Meli V, Castellani N, Martin S, Nodin JF, Bourgeois G, Portal JM, Querlioz D, Vianello E. Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks. Nat Commun 2023; 14:7530. [PMID: 37985669 PMCID: PMC10661910 DOI: 10.1038/s41467-023-43317-9] [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: 01/09/2023] [Accepted: 11/07/2023] [Indexed: 11/22/2023] Open
Abstract
Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive uncertainty assessment. However, because of their probabilistic nature, they are computationally intensive. An innovative solution utilizes memristors' inherent probabilistic nature to implement Bayesian neural networks. However, when using memristors, statistical effects follow the laws of device physics, whereas in Bayesian neural networks, those effects can take arbitrary shapes. This work overcome this difficulty by adopting a variational inference training augmented by a "technological loss", incorporating memristor physics. This technique enabled programming a Bayesian neural network on 75 crossbar arrays of 1,024 memristors, incorporating CMOS periphery for in-memory computing. The experimental neural network classified heartbeats with high accuracy, and estimated the certainty of its predictions. The results reveal orders-of-magnitude improvement in inference energy efficiency compared to a microcontroller or an embedded graphics processing unit performing the same task.
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Affiliation(s)
- Djohan Bonnet
- Université Grenoble Alpes, CEA, LETI, Grenoble, France.
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France.
| | | | - Atreya Majumdar
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France
| | | | | | | | | | - Simon Martin
- Université Grenoble Alpes, CEA, LETI, Grenoble, France
| | | | | | - Jean-Michel Portal
- Aix-Marseille Université, CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Marseille, France
| | - Damien Querlioz
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France.
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8
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Guo Y, Duan W, Liu X, Wang X, Wang L, Duan S, Ma C, Li H. Generative complex networks within a dynamic memristor with intrinsic variability. Nat Commun 2023; 14:6134. [PMID: 37783711 PMCID: PMC10545788 DOI: 10.1038/s41467-023-41921-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/21/2023] [Indexed: 10/04/2023] Open
Abstract
Artificial neural networks (ANNs) have gained considerable momentum in the past decade. Although at first the main task of the ANN paradigm was to tune the connection weights in fixed-architecture networks, there has recently been growing interest in evolving network architectures toward the goal of creating artificial general intelligence. Lagging behind this trend, current ANN hardware struggles for a balance between flexibility and efficiency but cannot achieve both. Here, we report on a novel approach for the on-demand generation of complex networks within a single memristor where multiple virtual nodes are created by time multiplexing and the non-trivial topological features, such as small-worldness, are generated by exploiting device dynamics with intrinsic cycle-to-cycle variability. When used for reservoir computing, memristive complex networks can achieve a noticeable increase in memory capacity a and respectable performance boost compared to conventional reservoirs trivially implemented as fully connected networks. This work expands the functionality of memristors for ANN computing.
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Affiliation(s)
- Yunpeng Guo
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, 100084, China
| | - Wenrui Duan
- School of Instrument Science and Opto Electronics Engineering, Laboratory of Intelligent Microsystems, Beijing Information Science & Technology University, Beijing, 100101, China.
| | - Xue Liu
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, 100084, China.
- School of Integrated Circuits, Tsinghua University, Beijing, 100084, China.
| | - Xinxin Wang
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, 100084, China
| | - Lidan Wang
- School of Artificial Intelligence, Southwest University, Chongqing, 400715, China
| | - Shukai Duan
- School of Artificial Intelligence, Southwest University, Chongqing, 400715, China
| | - Cheng Ma
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, 100084, China.
| | - Huanglong Li
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, 100084, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
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9
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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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10
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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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11
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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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12
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Chen S, Zhang T, Tappertzhofen S, Yang Y, Valov I. Electrochemical-Memristor-Based Artificial Neurons and Synapses-Fundamentals, Applications, and Challenges. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2301924. [PMID: 37199224 DOI: 10.1002/adma.202301924] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/22/2023] [Indexed: 05/19/2023]
Abstract
Artificial neurons and synapses are considered essential for the progress of the future brain-inspired computing, based on beyond von Neumann architectures. Here, a discussion on the common electrochemical fundamentals of biological and artificial cells is provided, focusing on their similarities with the redox-based memristive devices. The driving forces behind the functionalities and the ways to control them by an electrochemical-materials approach are presented. Factors such as the chemical symmetry of the electrodes, doping of the solid electrolyte, concentration gradients, and excess surface energy are discussed as essential to understand, predict, and design artificial neurons and synapses. A variety of two- and three-terminal memristive devices and memristive architectures are presented and their application for solving various problems is shown. The work provides an overview of the current understandings on the complex processes of neural signal generation and transmission in both biological and artificial cells and presents the state-of-the-art applications, including signal transmission between biological and artificial cells. This example is showcasing the possibility for creating bioelectronic interfaces and integrating artificial circuits in biological systems. Prospectives and challenges of the modern technology toward low-power, high-information-density circuits are highlighted.
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Affiliation(s)
- Shaochuan Chen
- Institute of Materials in Electrical Engineering 2 (IWE2), RWTH Aachen University, Sommerfeldstraße 24, 52074, Aachen, Germany
| | - Teng Zhang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Stefan Tappertzhofen
- Chair for Micro- and Nanoelectronics, Department of Electrical Engineering and Information Technology, TU Dortmund University, Martin-Schmeisser-Weg 4-6, D-44227, Dortmund, Germany
| | - Yuchao Yang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), School of Integrated Circuits, Peking University, Beijing, 100871, China
- School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China
- Center for Brain Inspired Intelligence, Chinese Institute for Brain Research (CIBR), Beijing, 102206, China
| | - Ilia Valov
- Peter Grünberg Institute (PGI-7), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
- Institute of Electrochemistry and Energy Systems "Acad. E. Budewski", Bulgarian Academy of Sciences, Acad. G. Bonchev 10, 1113, Sofia, Bulgaria
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13
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Ravichandran H, Knobloch T, Pannone A, Karl A, Stampfer B, Waldhoer D, Zheng Y, Sakib NU, Karim Sadaf MU, Pendurthi R, Torsi R, Robinson JA, Grasser T, Das S. Observation of Rich Defect Dynamics in Monolayer MoS 2. ACS NANO 2023. [PMID: 37490390 DOI: 10.1021/acsnano.2c12900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Defects play a pivotal role in limiting the performance and reliability of nanoscale devices. Field-effect transistors (FETs) based on atomically thin two-dimensional (2D) semiconductors such as monolayer MoS2 are no exception. Probing defect dynamics in 2D FETs is therefore of significant interest. Here, we present a comprehensive insight into various defect dynamics observed in monolayer MoS2 FETs at varying gate biases and temperatures. The measured source-to-drain currents exhibit random telegraph signals (RTS) owing to the transfer of charges between the semiconducting channel and individual defects. Based on the modeled temperature and gate bias dependence, oxygen vacancies or aluminum interstitials are probable defect candidates. Several types of RTSs are observed including anomalous RTS and giant RTS indicating local current crowding effects and rich defect dynamics in monolayer MoS2 FETs. This study explores defect dynamics in large area-grown monolayer MoS2 with ALD-grown Al2O3 as the gate dielectric.
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Affiliation(s)
- Harikrishnan Ravichandran
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Theresia Knobloch
- Institute for Microelectronics (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
| | - Andrew Pannone
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Alexander Karl
- Institute for Microelectronics (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
| | - Bernhard Stampfer
- Institute for Microelectronics (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
| | - Dominic Waldhoer
- Institute for Microelectronics (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
| | - Yikai Zheng
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Najam U Sakib
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Muhtasim Ul Karim Sadaf
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Rahul Pendurthi
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Riccardo Torsi
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
| | - Joshua A Robinson
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Physics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Tibor Grasser
- Institute for Microelectronics (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Materials Research Institute, Penn State University, University Park, Pennsylvania 16802, United States
- Electrical Engineering, Penn State University, University Park, Pennsylvania 16802, United States
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14
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Lau CS, Das S, Verzhbitskiy IA, Huang D, Zhang Y, Talha-Dean T, Fu W, Venkatakrishnarao D, Johnson Goh KE. Dielectrics for Two-Dimensional Transition-Metal Dichalcogenide Applications. ACS NANO 2023. [PMID: 37257134 DOI: 10.1021/acsnano.3c03455] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Despite over a decade of intense research efforts, the full potential of two-dimensional transition-metal dichalcogenides continues to be limited by major challenges. The lack of compatible and scalable dielectric materials and integration techniques restrict device performances and their commercial applications. Conventional dielectric integration techniques for bulk semiconductors are difficult to adapt for atomically thin two-dimensional materials. This review provides a brief introduction into various common and emerging dielectric synthesis and integration techniques and discusses their applicability for 2D transition metal dichalcogenides. Dielectric integration for various applications is reviewed in subsequent sections including nanoelectronics, optoelectronics, flexible electronics, valleytronics, biosensing, quantum information processing, and quantum sensing. For each application, we introduce basic device working principles, discuss the specific dielectric requirements, review current progress, present key challenges, and offer insights into future prospects and opportunities.
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Affiliation(s)
- Chit Siong Lau
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Sarthak Das
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Ivan A Verzhbitskiy
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Ding Huang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Yiyu Zhang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Teymour Talha-Dean
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
- Department of Physics and Astronomy, Queen Mary University of London, London E1 4NS, United Kingdom
| | - Wei Fu
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Dasari Venkatakrishnarao
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Kuan Eng Johnson Goh
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
- Department of Physics, National University of Singapore, 2 Science Drive 3, 117551, Singapore
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 50 Nanyang Avenue 639798, Singapore
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15
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Wali A, Das S. Hardware and Information Security Primitives Based on 2D Materials and Devices. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205365. [PMID: 36564174 DOI: 10.1002/adma.202205365] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 12/01/2022] [Indexed: 05/05/2023]
Abstract
Hardware security is a major concern for the entire semiconductor ecosystem that accounts for billions of dollars in annual losses. Similarly, information security is a critical need for the rapidly proliferating edge devices that continuously collect and communicate a massive volume of data. While silicon-based complementary metal-oxide-semiconductor technology offers security solutions, these are largely inadequate, inefficient, and often inconclusive, as well as resource intensive in time, energy, and cost, leading to tremendous room for innovation in this field. Furthermore, silicon-based security primitives have shown vulnerability to machine learning (ML) attacks. In recent years, 2D materials such as graphene and transition metal dichalcogenides have been intensely explored to mitigate these security challenges. In this review, 2D-materials-based hardware security solutions such as camouflaging, true random number generation, watermarking, anticounterfeiting, physically unclonable functions, and logic locking of integrated circuits (ICs) are summarized with accompanying discussion on their reliability and resilience to ML attacks. In addition, the role of native defects in 2D materials in developing high entropy hardware security primitives is also examined. Finally, the existing challenges for 2D materials, which must be overcome for large-scale deployment of 2D ICs to meet the security needs of the semiconductor industry, are discussed.
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Affiliation(s)
- Akshay Wali
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, 16802, USA
| | - Saptarshi Das
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, 16802, USA
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- Materials Research Institute, Penn State University, University Park, PA, 16802, USA
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16
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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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17
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Ravichandran H, Zheng Y, Schranghamer TF, Trainor N, Redwing JM, Das S. A Monolithic Stochastic Computing Architecture for Energy Efficient Arithmetic. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2206168. [PMID: 36308032 DOI: 10.1002/adma.202206168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/04/2022] [Indexed: 06/16/2023]
Abstract
As the energy and hardware investments necessary for conventional high-precision digital computing continue to explode in the era of artificial intelligence (AI), a change in paradigm that can trade precision for energy and resource efficiency is being sought for many computing applications. Stochastic computing (SC) is an attractive alternative since, unlike digital computers, which require many logic gates and a high transistor volume to perform basic arithmetic operations such as addition, subtraction, multiplication, sorting, etc., SC can implement the same using simple logic gates. While it is possible to accelerate SC using traditional silicon complementary metal-oxide-semiconductor (CMOS) technology, the need for extensive hardware investment to generate stochastic bits (s-bits), the fundamental computing primitive for SC, makes it less attractive. Memristor and spin-based devices offer natural randomness but depend on hybrid designs involving CMOS peripherals for accelerating SC, which increases area and energy burden. Here, the limitations of existing and emerging technologies are overcome, and a standalone SC architecture embedded in memory and based on 2D memtransistors is experimentally demonstrated. The monolithic and non-von-Neumann SC architecture occupies a small hardware footprint and consumes a miniscule amount of energy (<1 nJ) for both s-bit generation and arithmetic operations, highlighting the benefits of SC.
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Affiliation(s)
| | - Yikai Zheng
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Thomas F Schranghamer
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Nicholas Trainor
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- Materials Research Institute, Penn State University, University Park, PA, 16802, USA
| | - Joan M Redwing
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- Materials Research Institute, Penn State University, University Park, PA, 16802, USA
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- Materials Research Institute, Penn State University, University Park, PA, 16802, USA
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, 16802, USA
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18
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Sebastian A, Pendurthi R, Kozhakhmetov A, Trainor N, Robinson JA, Redwing JM, Das S. Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks. Nat Commun 2022; 13:6139. [PMID: 36253370 PMCID: PMC9576759 DOI: 10.1038/s41467-022-33699-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 09/27/2022] [Indexed: 12/24/2022] Open
Abstract
Artificial neural networks have demonstrated superiority over traditional computing architectures in tasks such as pattern classification and learning. However, they do not measure uncertainty in predictions, and hence they can make wrong predictions with high confidence, which can be detrimental for many mission-critical applications. In contrast, Bayesian neural networks (BNNs) naturally include such uncertainty in their model, as the weights are represented by probability distributions (e.g. Gaussian distribution). Here we introduce three-terminal memtransistors based on two-dimensional (2D) materials, which can emulate both probabilistic synapses as well as reconfigurable neurons. The cycle-to-cycle variation in the programming of the 2D memtransistor is exploited to achieve Gaussian random number generator-based synapses, whereas 2D memtransistor based integrated circuits are used to obtain neurons with hyperbolic tangent and sigmoid activation functions. Finally, memtransistor-based synapses and neurons are combined in a crossbar array architecture to realize a BNN accelerator for a data classification task.
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Affiliation(s)
- Amritanand Sebastian
- grid.29857.310000 0001 2097 4281Deparment of Engineering Science and Mechanics, Penn State University, University Park, PA 16802 USA
| | - Rahul Pendurthi
- grid.29857.310000 0001 2097 4281Deparment of Engineering Science and Mechanics, Penn State University, University Park, PA 16802 USA
| | - Azimkhan Kozhakhmetov
- grid.29857.310000 0001 2097 4281Department of Materials Science and Engineering, Penn State University, University Park, PA 16802 USA
| | - Nicholas Trainor
- grid.29857.310000 0001 2097 4281Department of Materials Science and Engineering, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 42812D Crystal Consortium Materials Innovation Platform, Penn State University, University Park, PA 16802 USA
| | - Joshua A. Robinson
- grid.29857.310000 0001 2097 4281Department of Materials Science and Engineering, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 4281Department of Chemistry, Penn State University, University Park, PA USA ,grid.29857.310000 0001 2097 4281Department of Physics, Penn State University, University Park, PA USA
| | - Joan M. Redwing
- grid.29857.310000 0001 2097 4281Department of Materials Science and Engineering, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 42812D Crystal Consortium Materials Innovation Platform, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 4281Department of Electrical Engineering and Computer Science, Penn State University, University Park, PA USA
| | - Saptarshi Das
- grid.29857.310000 0001 2097 4281Deparment of Engineering Science and Mechanics, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 4281Department of Materials Science and Engineering, Penn State University, University Park, PA 16802 USA ,grid.29857.310000 0001 2097 4281Department of Electrical Engineering and Computer Science, Penn State University, University Park, PA USA
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