Using Long Short-Term Memory (LSTM) Network to Predict Negative-Bias Temperature Instability
Publication Date
9-27-2021
Document Type
Conference Proceeding
Publication Title
2021 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)
DOI
10.1109/SISPAD54002.2021.9592531
First Page
60
Last Page
63
Abstract
In this paper, Long Short-Term Memory (LSTM) is used to predict transistor degradation due to Negative-Bias Temperature Instability (NBTI). The LSTM is trained by Technology Computer-Aided Design (TCAD) generated NBTI data and then used to predict the future degradation based on the future stress pattern (i.e. the future gate voltage sequence). It is also used to predict the degradation due to other random stress patterns at different frequencies. It is found that the LSTM trained by NBTI data due to random gate pulses at 100MHz clock frequency can 1) predict the NBTI due to other random gate pulses, 2) predict the NBTI up to 2 times longer time than it is trained for, and 3) predict the NBTI of 10 times higher and lower clock frequencies. Moreover, it can capture the Transient Trap Occupancy Model (TTOM) and Activated Barrier Double Well Thermionic (ABDWT) models well. It is shown that the framework works for both 2D and 3D simulations and, thus, can save a substantial amount of TCAD simulation time.
Funding Sponsor
San José State University
Keywords
Degradation, Long Short-Term Memory (LSTM), Negative-Bias Temperature Instability (NBTI), Reliability, Technology Computer-Aided Design (TCAD)
Department
Electrical Engineering
Recommended Citation
Fanus Arefaine, Meng Duan, Ravi Tiwari, Aadit Kapoor, Lee Smith, Souvik Mahapatra, and Hiu Yung Wong. "Using Long Short-Term Memory (LSTM) Network to Predict Negative-Bias Temperature Instability" 2021 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD) (2021): 60-63. https://doi.org/10.1109/SISPAD54002.2021.9592531
