基于神经网络的28nm FDSOI mosfet单事件瞬态建模

IF 1.6 4区工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Numerical Modelling-Electronic Networks Devices and Fields Pub Date : 2025-04-09 DOI:10.1002/jnm.70050

Jiaxin Chen, Ting Xu, Xinyi Zhang, Bo Li, Lei Wang, Jianhui Bu

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引用次数: 0

摘要

传统的单事件瞬态 (SET) SPICE 建模很难准确考虑各种工作因素。本文提出了一种基于神经网络的新方法。所提出的方法能将漏极电压、线性能量传递（LET）、温度、击穿位置、时间和漏极瞬态电流之间错综复杂的数据关联统一到一个单一模型中，而且精度很高。技术计算机辅助设计（TCAD）模拟用于获取原始 SET 数据进行训练。本文建立的遗传算法（GA）优化反向传播（BP）神经网络的均方根误差（RMSE）小于 2.0042%。该优化神经网络通过 Verilog-A 语言转换为 SET 电流 SPICE 模型，其实用性已通过双输入 NAND 门的电路仿真得到验证。

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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Modeling Single Event Transient in 28 Nm FDSOI MOSFETs Using a Neural Network Approach

It's hard to accurately consider various operating factors for the traditional single event transient (SET) SPICE modeling. This paper proposes a novel method based on a neural network. The proposed method can unify the intricate data correlations among drain voltage, linear energy transfer (LET), temperature, strike position, time, and drain transient current in a single model with high accuracy. Technology computer aided design (TCAD) simulation is used to get the original SET data for training. The genetic algorithm (GA) optimized back propagation (BP) neural network established herein has a root mean square error (RMSE) of less than 2.0042%. This optimized neural network is converted to the SET current SPICE model through the Verilog-A language, and its practicality has been verified through circuit simulation of a two-input NAND gate.

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来源期刊

International Journal of Numerical Modelling-Electronic Networks Devices and Fields 工程技术-工程：电子与电气

CiteScore

4.60

自引率

6.20%

发文量

101

审稿时长

>12 weeks

期刊介绍： Prediction through modelling forms the basis of engineering design. The computational power at the fingertips of the professional engineer is increasing enormously and techniques for computer simulation are changing rapidly. Engineers need models which relate to their design area and which are adaptable to new design concepts. They also need efficient and friendly ways of presenting, viewing and transmitting the data associated with their models. The International Journal of Numerical Modelling: Electronic Networks, Devices and Fields provides a communication vehicle for numerical modelling methods and data preparation methods associated with electrical and electronic circuits and fields. It concentrates on numerical modelling rather than abstract numerical mathematics. Contributions on numerical modelling will cover the entire subject of electrical and electronic engineering. They will range from electrical distribution networks to integrated circuits on VLSI design, and from static electric and magnetic fields through microwaves to optical design. They will also include the use of electrical networks as a modelling medium.