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A Standard and Reliable Method to Fabricate Two-Dimensional Nanoelectronics

A Standard and Reliable Method to Fabricate Two-Dimensional Nanoelectronics

Published on: August 28, 2018

An artificial neural network chip based on two-dimensional semiconductor.

Shunli Ma¹, Tianxiang Wu¹, Xinyu Chen¹

¹State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.

Science Bulletin

|December 22, 2022

Summary

This summary is machine-generated.

Researchers developed the first functional molybdenum disulfide (MoS₂) artificial neural network (ANN) chip. This 2D semiconductor chip achieves over 97% accuracy in tactile digit recognition, paving the way for AI computation.

Keywords:

Artificial neural network (ANN)Circuits MoS(2)Multiply-and-accumulate (MAC)Two-dimensional (2D) FETs

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Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

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Published on: April 15, 2015

Area of Science:

Materials Science
Electrical Engineering
Computer Science

Background:

Research on 2D semiconductors is advancing from fundamental studies to functional circuits.
Molybdenum disulfide (MoS₂) is a promising 2D material for electronic applications.

Purpose of the Study:

To demonstrate the first functional artificial neural network (ANN) chip based on MoS₂.
To integrate essential ANN components like multiply-and-accumulate (MAC), memory, and activation functions onto a single chip.

Main Methods:

Fabrication of 818 field-effect transistors (FETs) using wafer-scale, high-homogeneity MoS₂ films.
Implementation of a gate-last process for top-gate structured FETs.
Design and optimization of analog ANN circuits using a 62-level SPICE model.

Main Results:

Successful fabrication of a functional MoS₂ ANN chip.
Demonstration of a tactile digit sensing recognition application with over 97% accuracy after training.
Validation of wafer-scale integration potential for 2D semiconductors.

Conclusions:

The MoS₂ ANN chip showcases the potential of 2D materials in integrated circuits.
This work lays the groundwork for future applications of 2D semiconductors in AI computation.