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01-02-2025

An ultra-fast and precise automatic design framework for predicting and constructing high-performance shallow-trench-isolation LDMOS devices

Authors: Chenggang Xu, Hongyu Tang, Yuxuan Zhu, Yue Cheng, Xuanzhi Jin, Dawei Gao, Yitao Ma, Kai Xu

Published in: Journal of Computational Electronics | Issue 1/2025

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Abstract

The shallow trench isolation-based laterally diffused metal–oxide–semiconductor (STI LDMOS) is a crucial device for power integrated circuits. In this article, a novel framework that integrates an optimal objective function, Bayesian optimization (BO) algorithm, and deep neural network (DNN) model is proposed to fully realize the automatic and optimal design of STI LDMOS devices. On the one hand, given the structure of the device, the DNN model in the proposed method can provide ultra-fast and highly accurate performance estimation including breakdown voltage (BV) and specific on-resistance (Ronsp). The experimental results demonstrate 98.68% average prediction accuracy for both BV and Ronsp, higher than that for other machine learning (ML) algorithms. On the other hand, to target the specified value of BV and Ronsp, the proposed framework can fully automatically and optimally design the precise device structure that simultaneously achieves the target performance with the optimal figure of merit (FOM) of the device. Compared to technology computer-aided design (TCAD), there is only a 0.002% error in FOM and a 2.83% average error in BV and Ronsp. Moreover, considering the training time of the DNN model, the proposed framework is 100 times as efficient as other conventional frameworks. Thus, this research provides the experimental groundwork for constructing an automatic design framework for an LDMOS device and opens new opportunities for accelerating the development of LDMOS technology in the future.

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Metadata
Title
An ultra-fast and precise automatic design framework for predicting and constructing high-performance shallow-trench-isolation LDMOS devices
Authors
Chenggang Xu
Hongyu Tang
Yuxuan Zhu
Yue Cheng
Xuanzhi Jin
Dawei Gao
Yitao Ma
Kai Xu
Publication date
01-02-2025
Publisher
Springer US
Published in
Journal of Computational Electronics / Issue 1/2025
Print ISSN: 1569-8025
Electronic ISSN: 1572-8137
DOI
https://doi.org/10.1007/s10825-024-02244-8