Ultra‐Flexible High‐Linearity Silicon Nanomembrane Synaptic Transistor Array

J Jiahao Zhu C Chen Liu R Ruiyi Gao (Fundamentals Department Air Force Engineering University Xi'an 710051 China) Y Yuming Zhang H Haonan Zhang (Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences) S Shiyuan Cheng D Dexing Liu J Jialiang Wang Q Qi Liu Z Zifan Wang (Department of Chemistry) X Xinwei Wang (Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education) Y Yufeng Jin (School of Electronic and Computer Engineering Peking University Shenzhen 518055 China) M Min Zhang

Abstract

AbstractThe increasing demand for mobile artificial intelligence applications has elevated edge computing to a prominent research area. Silicon materials, renowned for their excellent electrical properties, are extensively utilized in traditional electronic devices. However, the development of silicon materials for flexible neuromorphic computing devices encounters great challenges. To address these limitations, ultrasoft silicon nanomembranes have emerged as a focal point due to their capability to preserve the superior electrical properties of silicon while providing substantial mechanical flexibility and interfacial tunability. Despite these advantages, difficulties remain in the transfer process of silicon nanomembranes and their integration for flexible synaptic transistors. In this work, an organic–inorganic hybrid polyimide‐Al2O3 dielectric layer has been designed for synaptic behavior grown by an atomic layer deposition process, and integrated with a silicon nanomembrane to realize highly flexible synaptic transistors. These transistors demonstrate stable electrical performance even after undergoing 10 000 bending cycles at an extreme curvature radius of 2.2 mm. Furthermore, the silicon nanomembrane transistors effectively emulate synaptic functions, exhibiting exceptional linearity in their long‐term characteristics, making them suitable for the application scenarios of detecting subtle signals. When applied to handwritten digit recognition simulations, these synaptic transistors have achieved a high accuracy rate of 93.2%.

Article Details

Volume / Issue Vol. 37, Issue 7
Published February 01, 2025
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (13)

J

Jiahao Zhu

C

Chen Liu

R

Ruiyi Gao

Fundamentals Department Air Force Engineering University Xi'an 710051 China

Y

Yuming Zhang

H

Haonan Zhang

Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences

S

Shiyuan Cheng

D

Dexing Liu

J

Jialiang Wang

Q

Qi Liu

Z

Zifan Wang

Department of Chemistry

X

Xinwei Wang

Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education

Y

Yufeng Jin

School of Electronic and Computer Engineering Peking University Shenzhen 518055 China

M

Min Zhang