MOSFET
神经形态工程学
MNIST数据库
肖特基势垒
材料科学
光电子学
计算机科学
晶体管
电子工程
电压
电气工程
工程类
人工智能
人工神经网络
二极管
作者
Faisal Bashir,Furqan Zahoor,Ali Alzahrani,Abdul Raouf Khan
出处
期刊:IEEE Transactions on Circuits and Systems Ii-express Briefs
[Institute of Electrical and Electronics Engineers]
日期:2023-06-16
卷期号:70 (11): 4018-4022
被引量:13
标识
DOI:10.1109/tcsii.2023.3286810
摘要
In this brief, a Schottky Barrier MOSFET (SB-MOSFET) based on Impact Ionization mechanism is used to design a leaky integrate and fire (LIF) neuron with considerable enhancement in area, energy and cost is proposed. Using 2D calibrated simulation, we confirmed that SB-MOSFET LIF is able to replicate the neuron behavior precisely without using external circuitry. The proposed LIF neuron shows significantly lower energy per spike of ~4 pJ/spike, which is lowest among the single transistor based neurons present in the literature. The recognition precision of 89.2% has been accomplished for Modified National Institute of Standards and Technology (MNIST) image. Besides this, SB-MOSFET doesn’t require any doped regions, therefore it can be fabricated with low thermal budget.
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