A Real-Time Diagnostic and Prognostic System for Plasma Etching

一个两阶段的研究项目将进行，其目标是使用光学发射光谱（OES）和原位工具数据描述的气体流量，射频功率，温度，压力和直流偏置电压的实时诊断和预测等离子体蚀刻设备的故障。 诊断系统将使用证据推理原理和神经网络来识别故障的过程中的“签名”固有的OES和工具数据。 该项目还将涉及开发一个能够预测集成电路晶片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来构建工具数据的时间序列模型，并且当模型预测指示数据中可疑趋势的演变时生成故障警报。 检测、分类和预测能力可以显著提高整体设备效率，减少停机时间，并减少半导体制造设备的废料。 该系统的成功开发和实施可以显著提高生产率，这将对半导体行业具有重要价值。

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.