A Real-Time Diagnostic and Prognostic System for Plasma Etching

等离子蚀刻的实时诊断和预测系统

• 批准号: 9806662
• 负责人: Gary May
• 金额: $ 15.58万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9806662&HistoricalAwards=false
• 关键词: Real; Time; Diagnostic; Prognostic; System

A two-phase research project will be undertaken for which the objective is to use optical emission spectroscopy (OES) and in-situ tool data describing gas flow rates, rf power, temperature, pressure and dc bias voltage for real-time diagnosis and prognosis of malfunctions in plasma etch equipment. The diagnostic system will use evidential reasoning principals and neural networks to identify malfunctions from the process "signatures" inherent in the OES and tool data. The project will also involve developing a prognostic system capable of predicting potential equipment malfunctions in the processing of integrated circuit (IC) wafers. The prognostic system will use neural networks to construct time series models of the tool data and generate a malfunction alarm when the model predictions indicate the evolution of suspicious trends in the data. The detection, classification, and prediction capabilities could result in dramatic improvements in overall equipment effectiveness, reduction in downtime, and reduction of scrap in semiconductor manufacturing equipment. The successful development and implementation of the developed system can lead to significant productivity enhancement, which will be of great value to the semiconductor industry.

将进行一个两阶段的研究项目，其目标是使用光学发射光谱（OES）和描述气体流速、射频功率、温度、压力和直流偏置电压的现场工具数据进行实时诊断和预测等离子蚀刻设备的故障。 诊断系统将使用证据推理原理和神经网络来从 OES 和工具数据固有的过程“签名”中识别故障。 该项目还将涉及开发一种能够预测集成电路（IC）晶圆加工过程中潜在设备故障的预测系统。 预测系统将使用神经网络构建工具数据的时间序列模型，并在模型预测表明数据中可疑趋势的演变时生成故障警报。 检测、分类和预测功能可以显着提高整体设备效率，减少停机时间，并减少半导体制造设备的废品。 所开发系统的成功开发和实施可以显着提高生产率，这对半导体行业具有巨大价值。