Search

This paper proposes a linear regression model for scalar-valued responses and two-way functional (bivariate) predictors. Our motivation stems from the quality evaluation of products based on optical emission spectroscopy data from virtual metrology of semiconductor manufacturing. We focus on multivariate cases where the smoothness and shapes of the data vary significantly across variables. We propose a two-step solution to this problem, consisting of decomposition and prediction. First, we decompose the two-way functional data into pairs of component functions using functional singular value decomposition. Next, we build functional linear models for the decomposed functional variables and obtain the final predictor by averaging the models. Results from numerical studies, including simulation studies and real data analysis, demonstrate the promising empirical properties of the proposed approach, especially when the number of predictors is large. [ABSTRACT FROM AUTHOR]

The "IEEE Transactions on Semiconductor Manufacturing" is a journal that publishes the latest advancements in the manufacturing of microelectronic and photonic components. It aims to enhance knowledge and improve manufacturing practices in the semiconductor industry. The journal covers various topics such as process integration, manufacturing equipment performance, yield analysis, metrology, and supply chain management. Papers submitted to the journal should focus on practical engineering techniques for solving manufacturing-related problems. The journal follows a peer-review process and encourages authors from low-income countries to submit their work. The standard length for regular papers is eight pages, and shorter contributions can be submitted as letters. The journal provides guidelines for manuscript preparation, including the use of the IEEE template style. It also accepts graphical abstracts and electronic supplements. Authors are responsible for preparing a publication-quality manuscript and may use English language editing services if needed. Plagiarism is strictly prohibited, and manuscripts found to have plagiarized content may be penalized. Authors are required to have an Open Researcher and Contributor ID (ORCID) and can submit their manuscripts online. The journal offers both traditional and open access publication options, with associated fees. Native language author names are supported, and page charges may apply for publication. The IEEE holds the copyright to the published material. [Extracted from the article]

As we enter a New Year, we can look back on another year of solid accomplishment at IEEE Transactions on Semiconductor Manufacturing. I am happy to report that our impact factor remains steady at 2.70, and our mean time to first decision remains competitive at 8.3 weeks. Our Editorial Board remains as strong as ever, with the addition of Dr. Jun-Haeng Lee in the area of machine learning and data science applications in 2023, and we are actively seeking new board members. Our submissions remain strong, as do the special sections from conferences (ASMC, ISSM and CS-MANTECH). The Special Issue on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing appeared in the November issue, and two additional special issues are in preparation. Prof. Duane Boning of MIT and Dr. Bill Nehrer of Technology Consultancy are co-editing a special issue on “Semiconductor Design for Manufacturing,” which will be a collaborative effort with the IEEE Transactions on Electron Devices. Drs. Oliver Patterson of Intel and Tomasz Brozek of PDF Solutions are also co-editing a special issue on sustainable semiconductor manufacturing. We are also happy to announce the Best paper Award for 2023, in the companion editorial appearing in this issue. Congratulations to all the honorees, and we hope we will continue to see their submissions in the future. Our thanks go to Drs. Jeanne Bickford, Dragan Djurdjanovic and Mahadeva Iyer Natarajan for their work on this committee. [ABSTRACT FROM AUTHOR]