高算力器件的发展使得人们愈发需要深化对智能制造工厂的全新认知并推动其建设。已有研究表明，带仿真器的离散事件模型对半导体芯片制造的架构规划、设计、搭建与运营至关重要。扩散机、离子注入机和光刻机因具备前馈与反馈连接特性，其工艺流程十分复杂。从工厂模型仿真中采集的数据集有望用于构建高价值的机器学习模型。作为基于代理数据的模型，这类模型的运行效率远高于基于物理的对应模型。为开发代理模型，拥有基于工厂模型构建、结构简洁且行为准确的公开基准仿真模型大有助益。因此，本研究基于英特尔（Intel）半导体制造工厂的基准模型，设计并构建了一个数据集。该模型采用并行离散事件系统规范（Parallel Discrete-Event System Specification）进行形式化定义，并通过DEVS-Suite仿真器运行。该时间序列数据集基于离散事件时间轨迹构建。研究进一步分析了该数据集，并将其用于开发基线单变量与多变量机器学习模型。该数据集也可供机器学习领域用于基于形式化、可扩展的组件化离散事件模型与仿真开展行为分析。
英文摘要：
Advancements in high-computing devices increase the necessity for improved and new understanding and development of smart manufacturing factories. Discrete-event models with simulators have been shown to be critical to architect, designing, building, and operating the manufacturing of semiconductor chips. The diffusion, implantation, and lithography machines have intricate processes due to their feedforward and feedback connectivity. The dataset collected from simulations of the factory models holds the promise of generating valuable machine-learning models. As surrogate data-based models, their executions are highly efficient compared to the physics-based counterpart models. For the development of surrogate models, it is beneficial to have publicly available benchmark simulation models that are grounded in factory models that have concise structures and accurate behaviors. Hence, in this research, a dataset is devised and constructed based on a benchmark model of an Intel semiconductor fabrication factory. The model is formalized using the Parallel Discrete-Event System Specification and executed using the DEVS-Suite simulator. The time series dataset is constructed using discrete-event time trajectories. This dataset is further analyzed and used to develop baseline univariate and multivariate machine learning models. The dataset can also be utilized in the machine learning community for behavioral analysis based on formalized and scalable component-based discrete-event models and simulations.
University of Klagenfurt（克拉根福大学）；Graz University of Technology（格拉茨技术大学）；Infineon Technologies（英飞凌科技）
Semiconductor manufacturing is a notoriously complex and costly multi-step process involving a long sequence of operations on expensive and quantity-limited equipment. Recent chip shortages and their impacts have highlighted the importance of semiconductors in the global supply chains and how reliant on those our daily lives are. Due to the investment cost, environmental impact, and time scale needed to build new factories, it is difficult to ramp up production when demand spikes. This work introduces a method to successfully learn to schedule a semiconductor manufacturing facility more effici…
The high-volume manufacturing of the next generation of semiconductor devices requires advances in measurement signal analysis. Many in the semiconductor manufacturing community have reservations about the adoption of deep learning; they instead prefer other model-based approaches for some image regression problems, and according to the 2021 IEEE International Roadmap for Devices and Systems (IRDS) report on Metrology a SEMI standardization committee may endorse this philosophy. The semiconductor manufacturing community does, however, communicate a need for state-of-the-art statistical analyse…
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）
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））
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）
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）
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）
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）
Smart Manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying Industrial Internet of Things (IIoT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing Machine Learning and Artificial Intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on Evolutionary Computing and Deep Learning algorithms…
Dynamic Multi-Criteria Bottleneck Severity Index (DMBSI) for Semiconductor Wafer Manufacturing: A Genetically Optimised Framework for Reentrant Production Systems
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）
Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contact
Data61, CSIRO（澳大利亚联邦科学与工业研究组织数据61分部）；Manufacturing, CSIRO（澳大利亚联邦科学与工业研究组织制造分部）；Songshan Lake Materials Laboratory（松山湖材料实验室）；Academy for Advanced Interdisciplinary Studies, Peking University（北京大学前沿交叉学科研究院）；School of Integrated Circuits, Peking University（北京大学集成电路学院）；Quantum Motion Ltd（量子运动有限公司）；City University of Hong Kong（香港城市大学）；The University of Melbourne（墨尔本大学）
An Unsupervised Time Series Anomaly Detection Approach for Efficient Online Process Monitoring of Additive Manufacturing
College of Engineering and Computer Science, University of Texas Rio Grande Valley（工程与计算机科学学院，德克萨斯理工大学Rio Grande Valley分校）；School of Industrial Engineering and Management, Oklahoma State University（工业工程与管理学院，俄克拉荷马州立大学）
