Dual‐Gated MoS2 Memtransistor Crossbar Array

Abstract

Memristive systems offer biomimetic functions that are being actively explored for energy‐efficient neuromorphic circuits. In addition to providing ultimate geometric scaling limits, 2D semiconductors enable unique gate‐tunable responses including the recent realization of hybrid memristor and transistor devices known as memtransistors. In particular, monolayer MoS2 memtransistors exhibit nonvolatile memristive switching where the resistance of each state is modulated by a gate terminal. Here, further control over the memtransistor neuromorphic response through the introduction of a second gate terminal is gained. The resulting dual‐gated memtransistors allow tunability over the learning rate for non‐Hebbian training where the long‐term potentiation and depression synaptic behavior is dictated by gate biases during the reading and writing processes. Furthermore, the electrostatic control provided by dual gates provides a compact solution to the sneak current problem in traditional memristor crossbar arrays. In this manner, dual gating facilitates the full utilization and integration of memtransistor functionality in highly scaled crossbar circuits. Furthermore, the tunability of long‐term potentiation yields improved linearity and symmetry of weight update rules that are utilized in simulated artificial neural networks to achieve a 94% recognition rate of hand‐written digits.

Document Details

Document Type
Pub Defense Publication
Publication Date
Sep 06, 2020
Source ID
10.1002/adfm.202003683

Entities

People

  • Hadallia Bergeron
  • Hong‐sub Lee
  • Hye Yun Jeong
  • Jiangtan Yuan
  • Katherine Su
  • Mark Hersam
  • Vinod K Sangwan
  • William A. Gaviria Rojas

Organizations

  • Kangwon National University
  • National Institute of Standards and Technology
  • National Research Foundation of Korea
  • National Science Foundation
  • Natural Sciences and Engineering Research Council
  • Northwestern University
  • Office of Naval Research

Tags

Readers

  • Integrated Circuit Design and Technology.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Biotechnology
  • Microelectronics
  • Microelectronics - Graphene