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具有与后端工艺兼容的一氧化锡薄膜晶体管的高维物理储能器

High-Dimensional Physical Reservoir with Back-End-of-Line-Compatible Tin Monoxide Thin-Film Transistor.

作者信息

Mun Sahngik A, Jang Yoon Ho, Han Janguk, Shim Sung Keun, Kang Sukin, Lee Yonghee, Choi Jinheon, Cheong Sunwoo, Lee Soo Hyung, Ryoo Seung Kyu, Han Joon-Kyu, Hwang Cheol Seong

机构信息

Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea.

System Semiconductor Engineering and Department of Electronic Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Republic of Korea.

出版信息

ACS Appl Mater Interfaces. 2024 Aug 14;16(32):42884-42893. doi: 10.1021/acsami.4c07747. Epub 2024 Aug 1.

DOI:10.1021/acsami.4c07747
PMID:39088726
Abstract

This work demonstrates a physical reservoir using a back-end-of-line compatible thin-film transistor (TFT) with tin monoxide (SnO) as the channel material for neuromorphic computing. The electron trapping and time-dependent detrapping at the channel interface induce the SnO·TFT to exhibit fading memory and nonlinearity characteristics, the critical assets for physical reservoir computing. The three-terminal configuration of the TFT allows the generation of higher-dimensional reservoir states by simultaneously adjusting the bias conditions of the gate and drain terminals, surpassing the performances of typical two-terminal-based reservoirs such as memristors. The high-dimensional SnO TFT reservoir performs exceptionally in two benchmark tests, achieving a 94.1% accuracy in Modified National Institute of Standards and Technology handwritten number recognition and a normalized root-mean-square error of 0.089 in Mackey-Glass time-series prediction. Furthermore, it is suitable for vertical integration because its fabrication temperature is <250 °C, providing the benefit of achieving a high integration density.

摘要

这项工作展示了一种物理储层,它使用与线后端兼容的薄膜晶体管(TFT),以一氧化锡(SnO)作为神经形态计算的沟道材料。沟道界面处的电子俘获和随时间变化的去俘获使得SnO·TFT表现出衰退记忆和非线性特性,这些是物理储层计算的关键特性。TFT的三端配置允许通过同时调整栅极和漏极端的偏置条件来生成更高维的储层状态,超越了诸如忆阻器等典型的基于两端的储层的性能。高维SnO TFT储层在两项基准测试中表现出色,在修改后的美国国家标准与技术研究院手写数字识别中达到了94.1%的准确率,在Mackey-Glass时间序列预测中归一化均方根误差为0.089。此外,由于其制造温度<250°C,它适用于垂直集成,具有实现高集成密度的优势。

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