C. Chen, M. Yang, S. Liu, T. Liu, K. Zhu, Y. Zhao, H. Wang, Q. Huang, R. Huang
{"title":"Bio-Inspired Neurons Based on Novel Leaky-FeFET with Ultra-Low Hardware Cost and Advanced Functionality for All-Ferroelectric Neural Network","authors":"C. Chen, M. Yang, S. Liu, T. Liu, K. Zhu, Y. Zhao, H. Wang, Q. Huang, R. Huang","doi":"10.23919/VLSIT.2019.8776495","DOIUrl":null,"url":null,"abstract":"For the brain-inspired neuromorphic computing, various emerging memory devices, including FeFET, have been applied to develop the artificial synapses, while the artificial neurons are still mostly CMOS-implemented and suffer from high-hardware-cost issue, especially when expanding advanced functions. In this work, a novel leaky-FeFET (L-FeFET) based on partially crystallized $\\text{Hf}_{05}\\text{z}_{\\text{r}05}\\text{O}_{2}$ layer is designed to mimic biological neurons. For the first time, we propose and experimentally demonstrate a capacitor-less L-FeFET neuron for basic leaky-integrate-and-fire function with ultra-low hardware cost of only one transistor and one resistor. Furthermore, a new hybrid L-FeFET-CMOS neuron is implemented to expand advanced spike-frequency adaption with almost half of hardware cost compared with CMOS neuron. This work provides a highly-integrated and inherently-low-energy implementation for neuron and the possibility for all-ferroelectric neural networks.","PeriodicalId":6752,"journal":{"name":"2019 Symposium on VLSI Technology","volume":"136 1","pages":"T136-T137"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Symposium on VLSI Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/VLSIT.2019.8776495","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 30
Abstract
For the brain-inspired neuromorphic computing, various emerging memory devices, including FeFET, have been applied to develop the artificial synapses, while the artificial neurons are still mostly CMOS-implemented and suffer from high-hardware-cost issue, especially when expanding advanced functions. In this work, a novel leaky-FeFET (L-FeFET) based on partially crystallized $\text{Hf}_{05}\text{z}_{\text{r}05}\text{O}_{2}$ layer is designed to mimic biological neurons. For the first time, we propose and experimentally demonstrate a capacitor-less L-FeFET neuron for basic leaky-integrate-and-fire function with ultra-low hardware cost of only one transistor and one resistor. Furthermore, a new hybrid L-FeFET-CMOS neuron is implemented to expand advanced spike-frequency adaption with almost half of hardware cost compared with CMOS neuron. This work provides a highly-integrated and inherently-low-energy implementation for neuron and the possibility for all-ferroelectric neural networks.