Advanced analysis of dynamic neural control advisories for process optimization and parts maintenance

J. Card, W. Chan, A. Cao, W. Martin, J. Morgan
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Abstract

This paper details an advanced set of analyses designed to drive specific process variable setpoint adjustments or maintenance actions required for cost effective process control using the Dynamic Neural Controller/spl trade/ (DNC) wafer-to-wafer advisories for semiconductor manufacturing advanced process control. The new analytic displays and metrics are illustrated using data obtained on a LAM 4520XL at STMicroelectronics as part of a SEMATECH SPIT beta test evaluation. The DNC represents a comprehensive modeling environment that uses as its input extensive process chamber information and history of the time since maintenance actions occurred. The DNC uses a neural network to predict multiple quality output metrics and a closed-loop risk-based optimization to maximize process quality performance while minimizing overall cost of tool operation and machine downtime. The software responds in an advisory mode on a wafer-to-wafer basis as to the optimal actions to be taken. In this paper, we present three specific instances of patterns arising during wafer processing over time that signal the process or equipment engineer to the need for corrective action: either a process setpoint adjustment or specific maintenance actions. Based on the controller's recommended corrective action set with the overall risk reduction predicted by such actions, a metric of corrective action "urgency" can be created. The tracking of this metric over time yields different pattern types that signify a quantified need for a specific type of corrective action. Three basic urgency patterns are found: 1. a pattern in a given maintenance action over time showing increasing urgency or "risk reduction" capability for the action; 2. a pattern in a process variable specific to a given recipe indicating a chronic request over time to only adjust the variable setpoint either above or below the current target; 3. a pattern in a process variable existing over all recipes processed through the chamber indicating chronic request to adjust the variable setpoint in either or both directions over time. This pattern is a pointer to the need for a maintenance action that is either corroborated by the urgency graph for that maintenance action, or if no such action has been previously taken, a guide to the source of the equipment malfunction.
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为工艺优化和零件维护提供动态神经控制咨询的高级分析
本文详细介绍了一套先进的分析,旨在使用动态神经控制器/spl贸易/ (DNC)半导体制造先进过程控制的晶圆到晶圆咨询,驱动特定的过程变量设定点调整或成本有效的过程控制所需的维护行动。作为SEMATECH SPIT beta测试评估的一部分,新的分析显示和指标使用意法半导体(STMicroelectronics)的LAM 4520XL上获得的数据进行说明。DNC代表了一个全面的建模环境,它使用大量的过程室信息和自维护操作发生以来的时间历史作为输入。DNC使用神经网络来预测多个质量输出指标和基于风险的闭环优化,以最大限度地提高过程质量性能,同时最大限度地降低工具操作和机器停机的总体成本。该软件以咨询模式响应在晶圆到晶圆的基础上采取的最佳行动。在本文中,我们提出了晶圆加工过程中出现的三个特定模式的实例,这些实例表明工艺或设备工程师需要采取纠正措施:要么是工艺设定值调整,要么是特定的维护行动。基于控制者推荐的纠正措施集,以及这些措施预测的整体风险降低,可以创建纠正措施“紧迫性”度量。随着时间的推移,对该度量的跟踪会产生不同的模式类型,这些模式类型表示对特定类型的纠正行动的量化需求。发现了三种基本的紧迫性模式:1。在给定的维护操作中,随着时间的推移显示出该操作的紧迫性或“风险降低”能力的模式;2. 特定于给定配方的过程变量中的模式,表明随着时间的推移,仅将变量设定值调整到高于或低于当前目标的长期请求;3.一种过程变量的模式,存在于通过该腔室处理的所有配方中,表明长期要求在一个或两个方向上调整变量设定值。这种模式是一个指针,表明需要进行维护行动,该维护行动的紧急程度图表可以证实该维护行动的必要性,如果以前没有采取过此类行动,则可以指导设备故障的根源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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