Generalized Linear Model-Based Process Control of Multivariate Measurements

本研究的目标是开发基于模型的控制算法的多个变量的测量时，正常和非正态分布的变量的混合物。 诸如此类的情况在工业中频繁出现，特别是在半导体工业中，其中制造和质量数据库除了产量和其他性能特性之外还包含诸如缺陷或颗粒污染物的计数和电参数的变量。 这些情况在化学和加工工业中也经常遇到。 该研究还可以将该算法扩展到级联过程，即具有顺序制造步骤和在每个步骤中发生的显著附加值处理的过程。 半导体制造、化学和加工工业的过程通常在多个过程步骤中顺序地增加价值。 在每个步骤中可能会测量几个过程和产品特性。在这种类型的过程中，上游变量子集的偏移可以传播到下游子集变量，尽管在后面的阶段过程没有任何问题。 一个战略，链接的步骤，以改善过程控制将被纳入这一算法。 如果成功的话，这项研究的结果将导致更好地利用数据，以改善各种类型的许多测量过程的控制。 随着自动化数据采集系统变得越来越普遍，更多的过程将需要所提出的分析类型。 此外，对级联过程的关注将通过考虑每个处理步骤与预期结果的偏差的分段设计来提高检测和隔离过程问题的能力。 这种方法应该提供对这些过程的更好的控制，以及对过程步骤之间的相互关系的更多了解。 这种不同测量类型在级联过程中的整合将为非标准数据的回归调整提供新的方法，并有助于统计偏差的研究。此外，预计这里开发的算法将成为其他研究人员用于后续扩展到特定级联过程的基准。

The objectives of this research are to develop model-based control algorithms for multiple variables that operate when the measurements are a mixture of normally and non-normally distributed variables. Situations such as this arise frequently in industry, notably in the semiconductor industry, where the manufacturing and quality databases contain variables such as counts of defects or particle contaminates and electrical parameters, in addition to yield and other performance characteristics. These situations are also frequently encountered in the chemical and process industries. The research may also extend this algorithm to cascade processes; that is, processes with sequential manufacturing steps and significant added value processing occurring at each step. Processes in semiconductor manufacturing and chemical and process industries often add value sequentially over a number of process steps. Several process and product characteristics might be measured at each step. In this type of process, a shift in an upstream subset of variables can propagate into downstream subset variables although nothing is wrong with the process at later stages. A strategy that links the steps to improve process control will be incorporated into this algorithm. If successful, the results of this research will lead to better use of the data to improve the control of processes with many measurements of various types. As automated data acquisition systems become more prevalent, more processes will require the type of analysis proposed. In addition, the focus on cascade processes will improve the ability to detect and isolate process problems through a segmented design that considers the deviance from expected results at each processing step. This approach should providebetter control of these processes, and greater knowledge of the interrelationships between process steps. This integration of different measurement types in cascade processes should lead to new methodology forthe regression-adjustment of nonstandard data and contribute to the research in statistical deviancies. Furthermore, it is expected that the algorithms developed here will become benchmarks for other researchers to use for subsequent extensions to particular cascade processes.