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We introduce ChatSQC, an innovative chatbot system that combines the power of OpenAI's Large Language Models (LLM) with a specific knowledge base in Statistical Quality Control (SQC). Our research focuses on enhancing LLMs using specific SQC references, shedding light on how data preprocessing parameters and LLM selection impact the quality of generated responses. By illustrating this process, we hope to motivate wider community engagement to refine LLM design and output appraisal techniques. We also highlight potential research opportunities within the SQC domain that can be facilitated by leveraging ChatSQC, thereby broadening the application spectrum of SQC. A primary goal of our work is to provide a template and proof-of-concept on how LLMs can be utilized by our community. To continuously improve ChatSQC, we ask the SQC community to provide feedback, highlight potential issues, request additional features, and/or contribute via pull requests through our public GitHub repository. Additionally, the team will continue to explore adding supplementary reference material that would further improve the contextual understanding of the chatbot. Overall, ChatSQC serves as a testament to the transformative potential of AI within SQC, and we hope it will spur further advancements in the integration of AI in this field. [ABSTRACT FROM AUTHOR]

The assumption of normality is usually tied to the design and analysis of an experimental study. However, when dealing with lifetime testing and censoring at fixed time intervals, we can no longer assume that the outcomes will be normally distributed. This generally requires the use of optimal design techniques to construct the test plan for specific distribution of interest. Optimal designs in this situation depend on the parameters of the distribution, which are generally unknown a priori. A Bayesian approach can be used by placing a prior distribution on the parameters, thereby leading to an appropriate selection of experimental design. This, along with the model and number of predictors, can be used to derive the D‐optimal design for an allowed number of experimental runs. This paper explores using this Bayesian approach on various lifetime regression models to select appropriate D‐optimal designs in regular and irregular design regions. [ABSTRACT FROM AUTHOR]

Most electronic devices are susceptible to electromagnetic interference (EMI); thus, it is necessary to recognize and identify the cause and effect of EMI as it can corrupt electronic signals and degrade equipment performance. Particularly, in semiconductor manufacturing, the equipment used for image capturing is subject to various noises induced by EMI, causing the image analysis to be unreliable during the image recognition and digitization process. Thus, in this research, we aim to detect and quantify the influence of EMI on semiconductor SEM (scanning electron microscope) images. For this, we apply several useful denoising and edge detection techniques to find a clearer distorted shape from EMI-generated images and then compute five shape-related measures to evaluate the distortion. From a comprehensive experimental analysis and statistical tests, it is found that the medians of all the extracted shape-related measures of high-EMI SEM images are higher than those of both medium- and weak-EMI SEM images, and also all the p-values of the statistical tests are close to 0, and thus we can conclude that all the measures are good quantification metrics for assessing the impact of EMI on semiconductor SEM images. [ABSTRACT FROM AUTHOR]

Generally, defective dies on semiconductor wafer maps tend to form spatial clusters in distinguishable patterns which contain crucial information on specific problems of equipment or process, thus it is highly important to identify and classify diverse defect patterns accurately. However, in practice, there exists a serious class imbalance problem, that is, the number of the defective dies on semiconductor wafer maps is usually much smaller than that of the non‐defective dies. In various machine learning applications, a typical classification algorithm is, however, developed under the assumption that the number of instances for each class is nearly balanced. If the conventional classification algorithm is applied to a class imbalanced dataset, it may lead to incorrect classification results and degrade the reliability of the classification algorithm. In this research, we consider the semiconductor wafer defect bin data combined with wafer warpage information and propose a new hybrid resampling algorithm to improve performance of classifiers. From the experimental analysis, we show that the proposed algorithm provides better classification performance compared to other data preprocessing methods regardless of classification models. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2010.06.021 Byline: James R. Broyles (a), Jeffery K. Cochran (b), Douglas C. Montgomery (a) Keywords: Markov chain; Regression; Transient inpatient inventory; Statistical inference Abstract: Inventory levels are critical to the operations, management, and capacity decisions of inventory systems but can be difficult to model in heterogeneous, non-stationary throughput systems. The inpatient hospital is a complicated throughput system and, like most inventory systems, hospitals dynamically make managerial decisions based on short term subjective demand predictions. Specifically, short term hospital staffing, resource capacity, and finance decisions are made according to hospital inpatient inventory predictions. Inpatient inventory systems have non-stationary patient arrival and service processes. Previously developed models present poor inventory predictions due to model subjectivity, high model complexity, solely expected value predictions, and assumed stationary arrival and service processes. Also, no models present statistical testing for model significance and quality-of-fit. This paper presents a Markov chain probability model that uses maximum likelihood regression to predict the expectations and discrete distributions of transient inpatient inventories. The approach has a foundation in throughput theory, has low model complexity, and provides statistical significance and quality-of-fit tests unique to this Markov chain. The Markov chain is shown to have superior predictability over Seasonal ARIMA models. Author Affiliation: (a) Department of Industrial Engineering, Arizona State University, Tempe, AZ 85287, USA (b) Air Force Institute of Technology, Department of Operational Sciences, 2950 Hobson Way, Wright-Patterson AFB, OH 45433-7765, USA Article History: Received 25 June 2009; Accepted 15 June 2010 Article Note: (footnote) [star] The views expressed in this article are those of the authors and do not reflect the official policy of the United States Air Force, Department of Defense, or the United States Government.