We have analyzed manufacturing data from several different semiconductor manufacturing plants, using decision tree induction software called Q-YIELD. The software generates rules for predicting when a given product should be rejected. The rules are intended to help the process engineers improve the yield of the product, by helping them to discover the causes of rejection. Experience with Q-YIELD has taught us the importance of data engineering -- preprocessing the data to enable or facilitate decision tree induction. This paper discusses some of the data engineering problems we have encountered with semiconductor manufacturing data. The paper deals with two broad classes of problems: engineering the features in a feature vector representation and engineering the definition of the target concept (the classes). Manufacturing process data present special problems for feature engineering, since the data have multiple levels of granularity (detail, resolution). Engineering the target concept is important, due to our focus on understanding the past, as opposed to the more common focus in machine learning on predicting the future.
Robust and Reliable AI for Predictive Quality in Semiconductor Materials Manufacturing with MLOps and Uncertainty Quantification
Merck Group（默克集团）；Versum Materials US LLC（Versum Materials美国分公司）；Merck Electronics KGaA（默克电子（KGaA））；Technical University of Darmstadt（达姆施塔特技术大学）；Otto von Guericke University Magdeburg（奥托·冯·格里克大学马格德堡分校）
Learning to Optimize Capacity Planning in Semiconductor Manufacturing
College of Computing and Data Science, Nanyang Technological University, Singapore（计算与数据科学学院，南洋理工大学，新加坡）；D-SIMLAB Technologies Pte Ltd, Singapore（D-SIMLAB技术私人有限公司，新加坡）
Tool-to-Tool Matching Analysis Based Difference Score Computation Methods for Semiconductor Manufacturing
Smart Equipment Solution Group (SESG), Samsung Semiconductor India Research (SSIR)（三星半导体印度研究（SSIR）智能设备解决方案组）；Mechatronics Research (MR), Device Solutions Research (DSR), Samsung Electronics Co.（三星电子机械研究（MR）、设备解决方案研究（DSR））
Tensor-based process control and monitoring for semiconductor manufacturing with unstable disturbances
Shanghai Jiao Tong University（上海交通大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction
School of Electrical and Computer Engineering, University of Oklahoma（俄克拉荷马大学电气与计算机工程学院）；Center for Quantum Research and Technology, University of Oklahoma（俄克拉荷马大学量子研究与技术中心）；Intelligent Neuromorphic and Quantum Understanding for Innovative Research and Engineering (INQUIRE) Laboratory（创新研究与工程智能神经形态与量子理解实验室）；Material Science and Engineering Program, University of Oklahoma, Norman, OK 73019 USA（俄克拉荷马大学材料科学与工程项目，Norman, OK 73019 USA）
AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes
University College Dublin（都柏林大学）；Macau University of Science and Technology（澳门科技大学）；New Jersey Institute of Technology（新泽西理工学院）；Royal College of Surgeons in Ireland（爱尔兰皇家外科医学院）
Automated Discovery of Laser Dicing Processes with Bayesian Optimization for Semiconductor Manufacturing
Department of Machine Learning and Neural Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University（机器学习与神经计算系，大脑、认知与行为研究所，拉德堡德大学）；ASMPT Center of Competency（ASMPT能力中心）
Machine Learning-Based Manufacturing Cost Prediction from 2D Engineering Drawings via Geometric Features
Institute for Data Science \& Artificial Intelligence, Bo g azi c i University, \. I stanbul, T\" u rkiye；ArtificaX Technologies, Bo g azi c i Teknopark, \. I stanbul, T\" u rkiye；Teknorot Automative Products Inc., D\" u zce, T\" u rkiye；\. I stanbul Ticaret University, Faculty of Engineering, Industrial Engineering, \. I stanbul, T\" u rkiye
Dynamic Multi-Criteria Bottleneck Severity Index (DMBSI) for Semiconductor Wafer Manufacturing: A Genetically Optimised Framework for Reentrant Production Systems
Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design
Knitting Robots: A Deep Learning Approach for Reverse-Engineering Fabric Patterns
Bharti School of Engineering and Computer Science, Laurentian University（劳伦森大学巴蒂工程与计算机科学学院）；Shanghai International Fashion Education Centre（上海国际时尚教育中心）
