半导体制造仿真的自动模型验证

Boon-Ping Gan, P. Lendermann, W. Scholl, Marcin Mosinski, P. Preuss
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引用次数: 1

摘要

英飞凌技术公司已经部署了短期模拟(STS),可提供工作中心性能的每日预测,用于运营决策。为了保证良好的预测精度,STS需要很高的建模保真度,需要良好的基础数据质量来建立模型。预测的准确性是通过自动模型验证(AMV)引擎来维持的。AMV监控并验证模拟与现实建模元素之间的差异,例如进程专用、正常运行时间、进程时间/吞吐量、采样率和批处理/流大小。它以多层视图,在不同的抽象层次上报告验证结果,并突出了仿真与现实之间的差距。用户可以快速识别空白并对错误进行纠正。在本文中,我们深入了解了AMV如何帮助检测数据问题的完整工作流程,解决这些问题的选项以及对模拟预测质量的积极影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic model verification for semiconductor manufacturing simulation
Short Term Simulation (STS) that provides daily forecasts of work center performance has been deployed in Infineon Technologies for operational decision makings. To ensure good forecast accuracy, the STS requires high modeling fidelity, requiring good basic data quality for model building. Forecast accuracy is maintained through an Automatic Model Verification (AMV) engine. The AMV monitors and verifies discrepancies between simulation and reality for modeling elements such as process dedication, uptime, process time/throughput, sampling rate, and batch/stream size. It reports the verification results with a multi-layered view, at different levels of abstraction, and the gaps between simulation and reality are highlighted. The user can quickly identify gaps and make correction to the errors. In this paper, we give an insight to the complete workflow on how AMV helps to detect data issues, the options to resolve such issues and the positive effect to the simulation forecast quality.
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