Artificial Neural Network-Based Screening Method for Short-Circuit Withstand Time in Packaged SiC MOSFETs to Enhance Device Consistency

IF 4.1 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Electron Device Letters Pub Date : 2025-04-21 DOI:10.1109/LED.2025.3562694

Hengyu Yu;Monikuntala Bhattacharya;Michael Jin;Limeng Shi;Shiva Houshmand;Atsushi Shimbori;Marvin H. White;Anant K. Agarwal

引用次数: 0

Abstract

Developing an effective methodology to enhance the uniformity of short-circuit withstand time (SCWT) in silicon carbide (SiC) MOSFETs is crucial for ensuring device reliability and consistency in power electronic systems. This letter presents a detailed analysis of SCWT variations caused by inevitable process-induced deviations and introduces a new screening approach based on artificial neural network (ANN) technology. A two-hidden-layer ANN model is constructed using characteristic parameters extracted from SiC MOSFETs. The trained model accurately predicts the SCWT of TCAD-simulated SiC MOSFETs, achieving a maximum error of less than 15% and an average error of only 2%. This proposed method effectively identifies and removes devices with shorter SCWT without compromising the performance of reliable devices, thereby enhancing post-fabrication consistency for packaged devices.

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基于人工神经网络的封装SiC mosfet耐短路时间筛选方法提高器件一致性

开发一种有效的方法来提高碳化硅（SiC） mosfet中短路耐受时间（SCWT）的均匀性对于确保电力电子系统中器件的可靠性和一致性至关重要。本文详细分析了由不可避免的过程诱导偏差引起的SCWT变化，并介绍了一种基于人工神经网络（ANN）技术的新筛选方法。利用从SiC mosfet中提取的特征参数，构建了两隐层神经网络模型。训练后的模型准确地预测了tcad模拟的SiC mosfet的SCWT，最大误差小于15%，平均误差仅为2%。该方法在不影响可靠器件性能的情况下，有效地识别和去除具有较短SCWT的器件，从而增强封装器件的制造后一致性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Electron Device Letters 工程技术-工程：电子与电气

CiteScore

8.20

自引率

10.20%

发文量

551

审稿时长

1.4 months

期刊介绍： IEEE Electron Device Letters publishes original and significant contributions relating to the theory, modeling, design, performance and reliability of electron and ion integrated circuit devices and interconnects, involving insulators, metals, organic materials, micro-plasmas, semiconductors, quantum-effect structures, vacuum devices, and emerging materials with applications in bioelectronics, biomedical electronics, computation, communications, displays, microelectromechanics, imaging, micro-actuators, nanoelectronics, optoelectronics, photovoltaics, power ICs and micro-sensors.