Probabilistic Generative Approach for Ambiguity-Aware Parameter Extraction - Zeng, Bolun; Tang, Zhenhua; Zhang, Yuanke; Li, Qingsong; Zhou, Changchun; Qiu, Liling; Chen, Yuefeng; Xiang, Zikun; Xu, Jun; Luo, Chao; Guo, Guoping (IEEE transactions on electron devices, 2025)
Artificial neural networks (ANNs) are increasingly used for parameter extraction in semiconductor device modeling. However, in practice, a parameter ambiguity issue arises, where multiple parameter combinations produce identical drain current values ( I_{\text {DS}} ). To address this challenge, we introduce a novel probabilistic generative framework. Specifically, we utilize a conditional variational autoencoder (CVAE) to learn the latent distribution of ambiguous parameters (e.g., threshold voltage and mobility) and generate diverse possible candidate parameter sets conditioned on I_{\text {DS}} characteristics. By validating the I_{\text {DS}} characteristics of the candidates, we ultimately select the optimal parameter set. Experiments across different device sizes, process technologies, and operating conditions demonstrate the effectiveness of our model.