Ting Xu, Yanping Guo, Jiaxin Chen, Jianhui Bu, Libo Gao, Tao Ni, Bo Li
{"title":"基于机器学习的多物理场 SET 建模方法","authors":"Ting Xu, Yanping Guo, Jiaxin Chen, Jianhui Bu, Libo Gao, Tao Ni, Bo Li","doi":"10.1002/jnm.3221","DOIUrl":null,"url":null,"abstract":"<p>With continuous downscaling of transistor sizes, the sensitivity to single event effect (SET) has become one of the most important reliability issues for aerospace integrated circuits. Besides the SET, integrated circuits will be affected by multiphysics such as temperature and voltage when working in space. Currently, the commonly used modeling methods are based on physical mechanisms and the double exponential pulse current. However, both methods are hard to build an accurate SET current model when various variables are considered. In this article, a novel machine learning modeling method of multiphysics SET is proposed, using intelligent algorithms to optimize the network also. With this method, we can obtain a reasonable and accurate multiphysics SET model based on neural network with single hidden layer, and there is no need to consider complex physical mechanisms. The model data is collected from TCAD simulation. Ant colony algorithm was used to optimize the initial values of the network. The RMS (root mean square error) of modeling result is less than 2%.</p>","PeriodicalId":50300,"journal":{"name":"International Journal of Numerical Modelling-Electronic Networks Devices and Fields","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A multiphysics SET modeling method based on machine learning\",\"authors\":\"Ting Xu, Yanping Guo, Jiaxin Chen, Jianhui Bu, Libo Gao, Tao Ni, Bo Li\",\"doi\":\"10.1002/jnm.3221\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>With continuous downscaling of transistor sizes, the sensitivity to single event effect (SET) has become one of the most important reliability issues for aerospace integrated circuits. Besides the SET, integrated circuits will be affected by multiphysics such as temperature and voltage when working in space. Currently, the commonly used modeling methods are based on physical mechanisms and the double exponential pulse current. However, both methods are hard to build an accurate SET current model when various variables are considered. In this article, a novel machine learning modeling method of multiphysics SET is proposed, using intelligent algorithms to optimize the network also. With this method, we can obtain a reasonable and accurate multiphysics SET model based on neural network with single hidden layer, and there is no need to consider complex physical mechanisms. The model data is collected from TCAD simulation. Ant colony algorithm was used to optimize the initial values of the network. The RMS (root mean square error) of modeling result is less than 2%.</p>\",\"PeriodicalId\":50300,\"journal\":{\"name\":\"International Journal of Numerical Modelling-Electronic Networks Devices and Fields\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-02-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Numerical Modelling-Electronic Networks Devices and Fields\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jnm.3221\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Numerical Modelling-Electronic Networks Devices and Fields","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jnm.3221","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
摘要
随着晶体管尺寸的不断缩小,对单事件效应(SET)的敏感性已成为航天集成电路最重要的可靠性问题之一。除 SET 外,集成电路在太空工作时还会受到温度和电压等多物理量的影响。目前,常用的建模方法基于物理机制和双指数脉冲电流。然而,当考虑到各种变量时,这两种方法都难以建立准确的 SET 电流模型。本文提出了一种新颖的多物理 SET 机器学习建模方法,利用智能算法对网络也进行了优化。利用这种方法,我们可以基于单隐层神经网络获得合理而精确的多物理场 SET 模型,而且无需考虑复杂的物理机制。模型数据来自 TCAD 仿真。采用蚁群算法优化网络的初始值。建模结果的 RMS(均方根误差)小于 2%。
A multiphysics SET modeling method based on machine learning
With continuous downscaling of transistor sizes, the sensitivity to single event effect (SET) has become one of the most important reliability issues for aerospace integrated circuits. Besides the SET, integrated circuits will be affected by multiphysics such as temperature and voltage when working in space. Currently, the commonly used modeling methods are based on physical mechanisms and the double exponential pulse current. However, both methods are hard to build an accurate SET current model when various variables are considered. In this article, a novel machine learning modeling method of multiphysics SET is proposed, using intelligent algorithms to optimize the network also. With this method, we can obtain a reasonable and accurate multiphysics SET model based on neural network with single hidden layer, and there is no need to consider complex physical mechanisms. The model data is collected from TCAD simulation. Ant colony algorithm was used to optimize the initial values of the network. The RMS (root mean square error) of modeling result is less than 2%.
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.