Probabilistic Generative Approach for Ambiguity-Aware Parameter Extraction

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 (IDS). 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 onIDScharacteristics. By validating theIDScharacteristics 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.