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Title: A Dual Magnetic Tunnel Junction‐Based Neuromorphic Device
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Author(s) / Creator(s):
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Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Advanced Intelligent Systems
Medium: X
Sponsoring Org:
National Science Foundation
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    CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm2switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.

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    Due to the increasing importance of artificial intelligence (AI), significant recent effort has been devoted to the development of neuromorphic circuits that seek to emulate the energy‐efficient information processing of the brain. While non‐volatile memory (NVM) based on resistive switches, phase‐change memory, and magnetic tunnel junctions has shown potential for implementing neural networks, additional multi‐terminal device concepts are required for more sophisticated bio‐realistic functions. Of particular interest are memtransistors based on low‐dimensional nanomaterials, which are capable of electrostatically tuning memory and learning behavior at the device level. Herein, a conceptual overview of the memtransistor is provided in the context of neuromorphic circuits. Recent progress is surveyed for memtransistors and related multi‐terminal NVM devices including dual‐gated floating‐gate memories, dual‐gated ferroelectric transistors, and dual‐gated van der Waals heterojunctions. The different materials systems and device architectures are classified based on the degree of control and relative tunability of synaptic behavior, with an emphasis on device concepts that harness the reduced dimensionality, weak electrostatic screening, and phase‐changes properties of nanomaterials. Finally, strategies for achieving wafer‐scale integration of memtransistors and multi‐terminal NVM devices are delineated, with specific attention given to the materials challenges for practical neuromorphic circuits.

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