Ferroelectric Schottky Barrier MOSFET as Analog Synapses for Neuromorphic Computing

Feng Xi, Andreas Grenmy, Jiayuan Zhang, Yisong Han, J. Bae, D. Grützmacher, Qing-Tai Zhao
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引用次数: 1

Abstract

In this paper, artificial synapses based on ferroelectric Schottky barrier MOSFETs (FE-SBFETs) are presented. The FE-SBFETs are fabricated with Si doped Hf02 ferroelectric layers scaling down to a gate length of 40 nm and using single crystalline NiSi2 contacts on siliconon-insulator (SOI) substrates. The ferroelectric polarization switching dynamics gradually modulate the Schottky barriers, thus programming the device conductance by applying stimulus on the gate to imitate the short- and long-term plasticity of biological synapse, including excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation (PPF) and long-term potentiation (LTP) and long-term depression (LTD) behaviors. Based on a multilayer perceptron artificial neural networks, a high recognition accuracy (83.6%) is achieved for handwritten digits. These findings demonstrate FE-SBFET has high potential as an ideal synaptic component for the future intelligent neuromorphic network.
作为神经形态计算模拟突触的铁电肖特基势垒MOSFET
本文介绍了一种基于铁电肖特基势垒mosfet (fe - sbfet)的人工突触。fe - sbfet采用掺Si的Hf02铁电层,栅极长度缩小到40 nm,并在硅绝缘体(SOI)衬底上使用单晶NiSi2触点。铁电极化开关动力学逐渐调节肖特基势垒,从而通过在栅极上施加刺激来模拟生物突触的短期和长期可塑性,包括兴奋性/抑制性突触后电流(EPSC/IPSC)、成对脉冲促进(PPF)和长期增强(LTP)以及长期抑制(LTD)行为,从而编程器件的电导。基于多层感知器的人工神经网络对手写数字的识别准确率达到了83.6%。这些研究结果表明,FE-SBFET作为未来智能神经形态网络的理想突触成分具有很高的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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