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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]

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]

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]

Semiconductor industry is experiencing a rising demand for environmentally friendly processes with the emphasis on green policies and worldwide environmental sustainability. Nitrogen trifluoride (NF3), the most common plasma chamber cleaning agent gas, poses a significant concern as a potent greenhouse gas since it has global warming potential (GWP), 740 times and 6 times higher than that CO2 and N2O. This study investigated the exhaust gas using quadrupole mass spectroscopy (QMS) and analyzed the change in cleaning speed and the type of exhaust gas through plasma monitoring using optical mass spectroscopy (OES). The objective is to lower the use of the amount of NF3 gas in chamber cleaning process to partially contribute the environmental sustainability in the point of semiconductor manufacturing. When a small amount of N2 was added to NF3 whose ratio of 7:23, the cleaning efficiency reached to 90% compared to NF3 gas alone. Addition of N2 positively affected electron density and temperature to increase the F-radical in remote plasma system. In conclusion, 18% of NF3 usage amount was reduced during the Sio2 deposition chamber cleaning process. [ABSTRACT FROM AUTHOR]

Curvilinear design was applied to standard cell layout to improve electrical characteristics and reduce manufacturing costs. Its implementation was intelligently co-optimized with 1-D Manhattan shapes and photolithography process to preserve the standard cell area equivalent to that of 1-D Manhattan-only designs. B-spline curve representation was employed to realize the curvilinear design. Curvilinear pathfinding was carried out through the Voronoi diagram to find the optimum routing path, and the A* routing algorithm to determine the shortest path. In the curvilinear-designed standard cells, the majority of standard cells exhibited reduced total metal length, decreased number of vias, and eliminated the need for an extra metal layer when compared to 1-D Manhattan-only standard cell designs. Manufacturability of curvilinear designs was evaluated, and potential solutions are proposed in the context of design rule, design rules check (DRC) and optical proximity correction (OPC). DRC and OPC were carried out within the currently employed electronic design automation (EDA) tools to verify the curvilinear designs. [ABSTRACT FROM AUTHOR]

Since the invention of transistors and integrated circuits, the development of semiconductor processes has advanced rapidly. Current microchips contain hundreds of millions of transistors. The remarkable development of semiconductors thus far has also led to difficulties in designing tightly packed lithography patterns without unwanted defects called hotspots in the manufacturing process. Therefore, research areas focusing on these problems have received much attention. In particular, predicting hotspots during the design stage is essential for high productivity in the semiconductor industry. In this study, we developed a deep learning-based SEM image generation model to predict hotspots from layout patterns at the design stage. Our model combines a segmentation network and an image-to-image translation network based on a conditional generative adversarial network in parallel. Our proposed model can predict and display potential hotspots in scanning electron microscopy images generated from given layouts. Additionally, the model leverages prior knowledge of the optical diameter to predict patterns that are prone to hotspots. Our model shows improved performance over baseline models when evaluated on real-world industrial data. [ABSTRACT FROM AUTHOR]

Semiconductor manufacturing demands a fast delivery of multiple gases to the tool. Hence this document provides formulas for the fluid-mechanical timescales of this delivery. This is done with a simple but realistic model of a gas-supply system, together with theory and computational-fluid-dynamic (CFD) simulations, and for representative but not comprehensive conditions relevant to etch. This timescale analysis shows that the rate-limiting process is (i) convection in the MFC-manifold tubing or (ii) convection in the tube between the flow splitter and the process chamber. This depends on (a) the lowest MFC sccm in the gas-supply system and (b) the total gas-supply-system sccm. Therefore, speeding up the gas delivery requires enhancing (i) and (ii). Moreover, (i) would become more important in view of a current trend towards smaller MFC sccms in etch. Examples on how to speed up the gas delivery and enhance the mixing are provided. The present analysis can be adapted to other conditions and manufacturing processes. [ABSTRACT FROM AUTHOR]

Accurate prediction of the remaining useful life (RUL) of an ion mill is vital for optimizing the overall performance of the ion mill etching (IME) process. However, due to the uneven distribution of important information, and the poorly understood failure mechanisms, fault prognosis in this process presents significant challenges. Deep neural networks have shown promising results for extracting, without domain knowledge, relevant features from condition monitoring data. This study proposes a multi-scale and multi-branch Transformer network based on the vanilla Transformer to predict the RUL of ion mills. To extract features on various scales, multi-scale feature extraction first generates receptive fields of various sizes, which are then integrated to obtain feature representations. The multi-branch Transformer uses the parallel attention mechanism and long short-term memory (LSTM) to capture both the adjacent location information and the crucial information of a given timestamp. Handcrafted features are also incorporated as additional input to enhance the prediction accuracy of the model. The proposed model is evaluated on a dataset from a semiconductor IME process. The experimental results demonstrate that the proposed model outperforms other deep neural network and further highlight the practical feasibility of the proposed method. [ABSTRACT FROM AUTHOR]