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Physics-based analysis and simulation model of electromagnetic interference induced soft logic upset in CMOS inverter 被引量:3
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作者 Yu-Qian Liu Chang-Chun Chai +4 位作者 Yu-Hang Zhang Chun-Lei Shi Yang Liu Qing-Yang Fan Yin-Tang Yang 《Chinese Physics B》 SCIE EI CAS CSCD 2018年第6期531-538,共8页
The instantaneous reversible soft logic upset induced by the electromagnetic interference(EMI) severely affects the performances and reliabilities of complementary metal–oxide–semiconductor(CMOS) inverters. This... The instantaneous reversible soft logic upset induced by the electromagnetic interference(EMI) severely affects the performances and reliabilities of complementary metal–oxide–semiconductor(CMOS) inverters. This kind of soft logic upset is investigated in theory and simulation. Physics-based analysis is performed, and the result shows that the upset is caused by the non-equilibrium carrier accumulation in channels, which can ultimately lead to an abnormal turn-on of specific metal–oxide–semiconductor field-effect transistor(MOSFET) in CMOS inverter. Then a soft logic upset simulation model is introduced. Using this model, analysis of upset characteristic reveals an increasing susceptibility under higher injection powers, which accords well with experimental results, and the influences of EMI frequency and device size are studied respectively using the same model. The research indicates that in a range from L waveband to C waveband, lower interference frequency and smaller device size are more likely to be affected by the soft logic upset. 展开更多
关键词 electromagnetic interference soft logic upset non-equilibrium carrier upset model
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单粒子翻转对神经网络的影响分析与优化
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作者 王慧玲 谢卓辰 梁旭文 《中国科学院大学学报(中英文)》 CSCD 北大核心 2021年第6期832-840,共9页
DNN芯片作为星载芯片应用到卫星系统中时会受到太空辐照的影响,其中单粒子翻转对存储单元的干扰会使得存储器单元的参数出现错误,该错误映射到神经网络中会造成神经网络最后的输出结果出现偏差。结合单粒子翻转概率模型,对用于网络推断... DNN芯片作为星载芯片应用到卫星系统中时会受到太空辐照的影响,其中单粒子翻转对存储单元的干扰会使得存储器单元的参数出现错误,该错误映射到神经网络中会造成神经网络最后的输出结果出现偏差。结合单粒子翻转概率模型,对用于网络推断的神经网络的权值参数进行注错后分析实验结果准确率,从激活函数的非线性特性分析并通过实验验证具有双边抑制效果的函数容错能力更强。进一步在网络卷积层后加入BN层和在训练过程中考虑L2正则化提高网络的容错能力,并通过实验验证其可行性。 展开更多
关键词 单粒子翻转错误概率模型 深度神经网络 激活函数 网络容错
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