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Machine Learning Model for Low Frequency Noise and Bias Temperature Instability  

Kim, Yongwoo (Department of System Semiconductor Engineering, Sangmyung University)
Lee, Jonghwan (Department of System Semiconductor Engineering, Sangmyung University)
Publication Information
Journal of the Semiconductor & Display Technology / v.19, no.4, 2020 , pp. 88-93 More about this Journal
Abstract
Based on the capture-emission energy (CEE) maps of CMOS devices, a physics-informed machine learning model for the bias temperature instability (BTI)-induced threshold voltage shifts and low frequency noise is presented. In order to incorporate physics theories into the machine learning model, the integration of artificial neural network (IANN) is employed for the computation of the threshold voltage shifts and low frequency noise. The model combines the computational efficiency of IANN with the optimal estimation of Gaussian mixture model (GMM) with soft clustering. It enables full lifetime prediction of BTI under various stress and recovery conditions and provides accurate prediction of the dynamic behavior of the original measured data.
Keywords
Capture-emission Energy Map; Bias Temperature Instability; Low Frequency Noise; Integration of Artificial Neural Network; Gaussian Mixture Model;
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Times Cited By KSCI : 4  (Citation Analysis)
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