可靠性(半导体)
过程(计算)
计算机科学
半导体器件制造
晶体管
集合(抽象数据类型)
工艺CAD
数据建模
机器学习
人工智能
电子工程
工程类
电气工程
工程制图
计算机辅助设计
软件工程
电压
薄脆饼
操作系统
功率(物理)
物理
量子力学
程序设计语言
作者
Paul Jungmann,Jeffrey B. Johnson,Eduardo C. Silva,William Taylor,Abdul Hanan Khan,Akash Kumar
出处
期刊:IEEE Transactions on Semiconductor Manufacturing
[Institute of Electrical and Electronics Engineers]
日期:2023-05-01
卷期号:36 (2): 268-278
标识
DOI:10.1109/tsm.2023.3240033
摘要
The requirements on data-driven Machine Learning models for industrial applications are often stricter, compared to those used for academic purposes, as model reliability is critical in industrial environments. Herein is introduced a framework which enables automated data generation with the goal of efficiently providing a data set sufficient to build a reliable and actionable model. Essential to this framework is the placement of the model training/testing data points, which need to be well distributed across the defined input parameter space. The framework is applied to semiconductor fabrication, wherein TCAD, a set of simulation tools that reproduce the physical processing and the final electrical performance of semiconductor devices, is a well-established capability. Transistor-level processing data is reproduced with TCAD simulations, from which the Machine Learning model is built. The framework described here assures that the resulting Machine Learning model fulfills the accuracy requirements across the parameter space. As an example application, the final Machine Learning model is then used to modify the process for a transistor, to obtain both better electrical performance and reduced variability.
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