Reliability prediction of semiconductor devices based on GRU-LSTM neural network

Shufeng Tang; Xuesong Xie; Xiaoling Zhang

doi:10.1117/12.3025192

8 February 2024 Reliability prediction of semiconductor devices based on GRU-LSTM neural network

Shufeng Tang, Xuesong Xie, Xiaoling Zhang

Proceedings Volume 13066, International Conference on Optoelectronic Materials and Devices (ICOMD 2023); 130660B (2024) https://doi.org/10.1117/12.3025192
Event: 2023 International Conference on Optoelectronic Materials and Devices (COMD 2023), 2023, Chongqing, China

Abstract

The reliability of semiconductor devices is a key indicator to measure the reliability of electronic devices. In view of the difficulties in modeling, low prediction accuracy and long prediction period faced by traditional reliability prediction methods of semiconductor devices, a deep learning based reliability prediction method of semiconductor devices is proposed in this paper. Besides, the accelerated degradation test data set of bipolar transistors under constant stress of temperature and humidity is analyzed, and the failure sensitive parameter I_cbo of transistors is determined. The Data Fitting, LSTM, GRU and GRU-LSTM models are used to predict the trend of I_cbo degradation of three transistors which are randomly selected from data set. The prediction results of device storage life using data fitting method and GRU-LSTM model are compared, and it is found that the overall distribution of device storage life predicted by the two methods is similar, but the prediction accuracy of GRU-LSTM model is higher and more suitable to the actual situation. This paper can provide some reference for predicting the reliability of domestic semiconductor devices.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shufeng Tang, Xuesong Xie, and Xiaoling Zhang "Reliability prediction of semiconductor devices based on GRU-LSTM neural network", Proc. SPIE 13066, International Conference on Optoelectronic Materials and Devices (ICOMD 2023), 130660B (8 February 2024); https://doi.org/10.1117/12.3025192

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KEYWORDS

Data modeling

Transistors

Semiconductors

Neural networks

Reliability

Deep learning

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