Yijoon Kim, Hyangwoo Kim, Kyounghwan Oh, Ju Hong Park, Byoung Don Kong, Chang-Ki Baek
{"title":"使用硅锗带隙工程电阻开关晶体管的低能耗可调 LIF 神经元。","authors":"Yijoon Kim, Hyangwoo Kim, Kyounghwan Oh, Ju Hong Park, Byoung Don Kong, Chang-Ki Baek","doi":"10.1186/s11671-024-04079-5","DOIUrl":null,"url":null,"abstract":"<div><p>We have proposed leaky integrate-and-fire (LIF) neuron having low-energy consumption and tunable functionality without external circuit components. Our LIF neuron has a simple configuration consisting of only three components: one bandgap-engineered resistive switching transistor (BE-RST), one capacitor, and one resistor. Here, the crucial point is that BE-RST with a silicon–germanium heterojunction possesses an amplified hysteric current switching with a low latch-up voltage due to improved hole storage capability and impact ionization coefficient. Therefore, the proposed neuron utilizing BE-RST requires an energy consumption of 0.36 pJ/spike, which is approximately six times lower than 2.08 pJ/spike of pure silicon-RST based neuron. In addition, the spiking properties can be tuned by modulating the leakage rate and threshold through gate bias, which contributes to energy-efficient sparse-activity and high learning accuracy. As a result, our proposed neuron can be a promising candidate for executing various spiking neural network applications.</p></div>","PeriodicalId":51136,"journal":{"name":"Nanoscale Research Letters","volume":"19 1","pages":""},"PeriodicalIF":5.5000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11343930/pdf/","citationCount":"0","resultStr":"{\"title\":\"Low-energy and tunable LIF neuron using SiGe bandgap-engineered resistive switching transistor\",\"authors\":\"Yijoon Kim, Hyangwoo Kim, Kyounghwan Oh, Ju Hong Park, Byoung Don Kong, Chang-Ki Baek\",\"doi\":\"10.1186/s11671-024-04079-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>We have proposed leaky integrate-and-fire (LIF) neuron having low-energy consumption and tunable functionality without external circuit components. Our LIF neuron has a simple configuration consisting of only three components: one bandgap-engineered resistive switching transistor (BE-RST), one capacitor, and one resistor. Here, the crucial point is that BE-RST with a silicon–germanium heterojunction possesses an amplified hysteric current switching with a low latch-up voltage due to improved hole storage capability and impact ionization coefficient. Therefore, the proposed neuron utilizing BE-RST requires an energy consumption of 0.36 pJ/spike, which is approximately six times lower than 2.08 pJ/spike of pure silicon-RST based neuron. In addition, the spiking properties can be tuned by modulating the leakage rate and threshold through gate bias, which contributes to energy-efficient sparse-activity and high learning accuracy. As a result, our proposed neuron can be a promising candidate for executing various spiking neural network applications.</p></div>\",\"PeriodicalId\":51136,\"journal\":{\"name\":\"Nanoscale Research Letters\",\"volume\":\"19 1\",\"pages\":\"\"},\"PeriodicalIF\":5.5000,\"publicationDate\":\"2024-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11343930/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nanoscale Research Letters\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://link.springer.com/article/10.1186/s11671-024-04079-5\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nanoscale Research Letters","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1186/s11671-024-04079-5","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Low-energy and tunable LIF neuron using SiGe bandgap-engineered resistive switching transistor
We have proposed leaky integrate-and-fire (LIF) neuron having low-energy consumption and tunable functionality without external circuit components. Our LIF neuron has a simple configuration consisting of only three components: one bandgap-engineered resistive switching transistor (BE-RST), one capacitor, and one resistor. Here, the crucial point is that BE-RST with a silicon–germanium heterojunction possesses an amplified hysteric current switching with a low latch-up voltage due to improved hole storage capability and impact ionization coefficient. Therefore, the proposed neuron utilizing BE-RST requires an energy consumption of 0.36 pJ/spike, which is approximately six times lower than 2.08 pJ/spike of pure silicon-RST based neuron. In addition, the spiking properties can be tuned by modulating the leakage rate and threshold through gate bias, which contributes to energy-efficient sparse-activity and high learning accuracy. As a result, our proposed neuron can be a promising candidate for executing various spiking neural network applications.
期刊介绍:
Nanoscale Research Letters (NRL) provides an interdisciplinary forum for communication of scientific and technological advances in the creation and use of objects at the nanometer scale. NRL is the first nanotechnology journal from a major publisher to be published with Open Access.