LSTM-based Fault Prediction Model of Semiconductor Device under Thermal Stress

doi:10.3969/j.issn.1000-1093.2021.06.017

Abstract

Abstract: For the semiconductor device failure caused by time and stress, starting with multi-source data, a fault predicition model of semiconductor device under thermal stress based on long short-term memory(LSTM) algorithm is proposed to study the change of semiconductor device status with thermal stress level and the cumulative duration of stress, and predict the failure time of semiconductor device. The proposed model uses the advantage of long-term memory ability of LSTM algorithm to build multi-source data stack structure and improve the ability of model fitting the state curve of semiconductor device. The high and low frequency noises are filtered by using the weighted moving average filtering method. The first-order predictor data compression algorithm is used to deal with the feature vectors with continuous and slow variation. And the experimental data is used to test and verify the model. The results show that the model can better reflect the variation trend of semiconductor device status under the action of thermal stress, and the prediction errors of five experiments are within 1.7%, which has high accuracy. The model can predict the failure time in advance, which verifies the feasibility and effectiveness of the proposed model.

Key words: semiconductordevice, thermalstress, faultprediction, longshort-termmemory

CLC Number:

TN386.1

ZHANG Mingyu, WANG Qi, YU Yang. LSTM-based Fault Prediction Model of Semiconductor Device under Thermal Stress[J]. Acta Armamentarii, 2021, 42(6): 1265-1274.

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