Heterosynaptic MoS2 Memtransistors Emulating Biological Neuromodulation for Energy‐Efficient Neuromorphic Electronics

Author:

Huh Woong1,Lee Donghun1,Jang Seonghoon1,Kang Jung Hoon1,Yoon Tae Hyun1,So Jae‐Pil2,Kim Yeon Ho1,Kim Jong Chan3,Park Hong‐Gyu2,Jeong Hu Young4,Wang Gunuk156,Lee Chul‐Ho7ORCID

Affiliation:

1. KU‐KIST Graduate School of Converging Science and Technology Korea University 145 Anam‐ro Seongbuk‐gu Seoul 02841 Republic of Korea

2. Department of Physics Korea University 145 Anam‐ro Seongbuk‐gu Seoul 02841 Republic of Korea

3. School of Materials Science and Engineering Ulsan National Institute of Science and Technology (UNIST) UNIST‐gil 50 Ulsan 44919 Republic of Korea

4. UNIST Central Research Facilities (UCRF) Ulsan National Institute of Science and Technology (UNIST) UNIST‐gil 50 Ulsan 44919 Republic of Korea

5. Department of Integrative Energy Engineering Korea University 145 Anam‐ro Seongbuk‐gu Seoul 02841 Republic of Korea

6. Center for Neuromorphic Engineering Korea Institute of Science and Technology 5 Hwarang‐ro 14‐gil, Seongbuk‐gu Seoul 02792 Republic of Korea

7. Department of Electrical and Computer Engineering Seoul National University 1 Gwanak‐ro Gwanak‐gu Seoul 08826 Republic of Korea

Abstract

AbstractHeterosynaptic neuromodulation is a key enabler for energy‐efficient and high‐level biological neural processing. However, such manifold synaptic modulation cannot be emulated using conventional memristors and synaptic transistors. Thus, reported herein is a three‐terminal heterosynaptic memtransistor using an intentional‐defect‐generated molybdenum disulfide channel. Particularly, the defect‐mediated space‐charge‐limited conduction in the ultrathin channel results in memristive switching characteristics between the source and drain terminals, which are further modulated using a gate terminal according to the gate‐tuned filling of trap states. The device acts as an artificial synapse controlled by sub‐femtojoule impulses from both the source and gate terminals, consuming lower energy than its biological counterpart. In particular, electrostatic gate modulation, corresponding to biological neuromodulation, additionally regulates the dynamic range and tuning rate of the synaptic weight, independent of the programming (source) impulses. Notably, this heterosynaptic modulation not only improves the learning accuracy and efficiency but also reduces energy consumption in the pattern recognition. Thus, the study presents a new route leading toward the realization of highly networked and energy‐efficient neuromorphic electronics.

Publisher

Wiley

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

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