The ability to exploit data-driven process control and decision making frameworks is rapidly becoming critical to success in semiconductor manufacturing. At the same time, advances in manufacturing equipment sensors has seen dramatic increases in sampling rates in recent years, which has led to the ability to capture transients effects in signals with higher fidelity than previously possible. It is known that data-driven process control and decision making methodologies rely on the process of extraction of useful information from raw data signals. To that end, the current manuscript presents a novel methodology for extraction of information from data in the form of a feature set that faithfully and reliably depicts both the transient and stationary portions of the signals. The solution proposed is an automated dynamics-inspired approach that looks to segment a signal into steady state and transient components before summarizing each segment into a set of relevant signatures. The steady state segments are summarized through a set of statistics and each transient is reduced to a set of parameters relating to the underlying system dynamics, such as settling time, rise time, overshoots, etc. The impactful novel information content of the resulting dynamics-inspired feature set is evaluated by application to chamber matching, product defect level prediction and product quality characteristic prediction in etch and deposition processes executed in various tools across several modern 300 mm fabs.

利用数据驱动的过程控制和决策框架的能力正迅速成为半导体制造成功的关键。同时，近年来，随着制造设备传感器的进步，采样率也有了显着提高，这使得能够以比以前更高的保真度捕获信号中的瞬态效应。众所周知，数据驱动的过程控制和决策方法依赖于从原始数据信号中提取有用信息的过程。为此，当前的手稿提出了一种新颖的方法，用于以特征集的形式从数据中提取信息，如实和可靠地描述了信号的瞬态和平稳部分。提出的解决方案是一种以动态动力学为灵感的方法，该方法在将每个段汇总为一组相关签名之前，将信号分为稳态和瞬态分量。通过一组统计数据汇总稳态段，并将每个瞬态简化为与底层系统动力学相关的一组参数，例如建立时间，上升时间，超调等。所产生的动力学中有影响力的新颖信息内容包括：启发性的功能集可通过在几家现代化的300毫米晶圆厂中以各种工具执行的蚀刻和沉积过程中的腔室匹配，产品缺陷水平预测和产品质量特征预测的应用中进行评估。