Feng Xi, Andreas Grenmy, Jiayuan Zhang, Yisong Han, J. Bae, D. Grützmacher, Qing-Tai Zhao
{"title":"Ferroelectric Schottky Barrier MOSFET as Analog Synapses for Neuromorphic Computing","authors":"Feng Xi, Andreas Grenmy, Jiayuan Zhang, Yisong Han, J. Bae, D. Grützmacher, Qing-Tai Zhao","doi":"10.1109/ESSCIRC55480.2022.9911305","DOIUrl":null,"url":null,"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.","PeriodicalId":168466,"journal":{"name":"ESSCIRC 2022- IEEE 48th European Solid State Circuits Conference (ESSCIRC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ESSCIRC 2022- IEEE 48th European Solid State Circuits Conference (ESSCIRC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ESSCIRC55480.2022.9911305","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.