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 (<inline-formula> <tex-math notation=&quo...

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Bibliographic Details
Published in:IEEE transactions on electron devices Vol. 72; no. 10; pp. 5544 - 5550
Main Authors: Zeng, Bolun, Tang, Zhenhua, Zhang, Yuanke, Li, Qingsong, Zhou, Changchun, Qiu, Liling, Chen, Yuefeng, Xiang, Zikun, Xu, Jun, Luo, Chao, Guo, Guoping
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Ambiguity
Artificial neural network (ANN)
Artificial neural networks
conditional variational autoencoder (CVAE)
Decoding
Gaussian distribution
Integrated circuit modeling
Mathematical models
Optimization
parameter ambiguity
Parameter extraction
Parameters
Probabilistic logic
Residual neural networks
Semiconductor device modeling
Semiconductor devices
Threshold voltage
Training
ISSN:0018-9383, 1557-9646
Online Access:Get full text
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Summary: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 (<inline-formula> <tex-math notation="LaTeX">{I}_{\text {DS}} </tex-math></inline-formula>). 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 <inline-formula> <tex-math notation="LaTeX">{I}_{\text {DS}} </tex-math></inline-formula> characteristics. By validating the <inline-formula> <tex-math notation="LaTeX">{I}_{\text {DS}} </tex-math></inline-formula> 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.
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ISSN:0018-9383
1557-9646
DOI:10.1109/TED.2025.3594676