Analyse eines digitalen Zwillings zur zeitkontinuierlichen Batchoptimierung in der Halbleiterfertigung

Motivated by the ongoing growth in demand for microchips, high production costs and the complex interplay of human, machine, material and method (4M), suppliers strive to develop more advanced production planning and control regimes for semiconductor production. Batching decisions often dramatically influence the overall performance of wafer fabs in terms of capacity utilization, due date compliance, cycle time and variability. To optimize such processes, we present an integrated testbed for batch formation optimization. Using a simulation of multiple semiconductor work centers, we explore how to optimize work in progress (WIP) flow with a continuous real-time scheduler and previously published batch formation heuristics. The proposed solver is designed to only optimize capacity-limited operations. By considering real-world operations requirements and semiconductor process specifics such as qualification criteria and re-entrance in our model, we demonstrate how to realize significant throughput gains. We explore and demonstrate the developed digital twin through a powerful BI frontend for historical analysis and real-time shop floor monitoring

Multiobjective scheduling for semiconductor manufacturing plants

Scheduling of semiconductor wafer manufacturing system is identified as a complex problem, involving multiple and conflicting objectives (minimization of facility average utilization, minimization of waiting time and storage, for instance) to simultaneously satisfy. In this study, we propose an efficient approach based on an artificial neural network technique embedded into a multiobjective genetic algorithm for multi-decision scheduling problems in a semiconductor wafer fabrication environment

반도체 공장 내 일시적인 생산 용량 확장 정책 제안

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2021. 2. 박건수.Due to the instability of the capacity of the semiconductor process, there are cases in which the production capacity temporarily becomes insufficient compared to the capacity allocated by the initial plan. To respond, production managers require capacity to other lines with compatible equipment. This decision can have an adverse effect on the entire line because the processes are connected in a sequence. In particular, it becomes more problematic when the machine group is a bottleneck process group. Therefore, this study proposes a capacity expansion policy learned by reinforcement learning algorithms in this environment using a FAB simulator built upon a WIP balancing scheduler and a machine disruption model. These policies performed better than policies imitating human decision in terms of throughput and machine efficiency.반도체공장은 설비 용량의 불안정성 때문에 초기 계획하여 할당된 설비 용량에 비해 일시적으로 생산 용량이 부족해지는 경우가 발생한다. 이를 대응하기 위해 생산 담당자들은 다른 라인에 호환가능한 설비를 공유하는 것을 요청하는데, 가능한 많은 양의 WIP에 대한 요청을 한다. 이러한 의사결정은 공정이 순차적으로 연결된 점 때문에 라인 전체 측면에서는 오히려 WIP Balancing을 악화시킬 수 있다. 특히 해당 공정군이 병목공정군인 경우 더 문제가 된다. 따라서 본 연구에서는 병목공정군을 중심으로 한 WIP Balancing scheduler를 이용하여 FAB simulator를 만든 뒤 이러한 환경속에서 강화학습 알고리즘으로 학습한 생산 용량 확장 정책을 제안한다. 이러한 정책은 throughput, machine efficiency 측면에서 사람의 의사결정을 모방한 정책보다 좋은 성과를 보였다.Abstract i Contents ii List of Tables iv List of Figures v Chapter 1 Introduction 1 1.1 Problem Description 3 1.2 Research Motivation and Contribution 5 1.3 Organization of the Thesis 5 Chapter 2 Literature Review 6 2.1 Review on FAB scheduling 6 2.2 Review on Dynamic production control 7 Chapter 3 Proposed Approach and Methodology 8 3.1 Proposed Approach 8 3.2 FAB Simulator 17 3.3 Reinforcement Learning Approach 26 Chapter 4 Computational Experiments 30 4.1 Experiment settings 30 4.2 Test Instances 31 4.3 Test Results 33 Chapter 5 Conclusions 37 Bibliography 38 국문초록 39Maste

Production Scheduling

Generally speaking, scheduling is the procedure of mapping a set of tasks or jobs (studied objects) to a set of target resources efficiently. More specifically, as a part of a larger planning and scheduling process, production scheduling is essential for the proper functioning of a manufacturing enterprise. This book presents ten chapters divided into five sections. Section 1 discusses rescheduling strategies, policies, and methods for production scheduling. Section 2 presents two chapters about flow shop scheduling. Section 3 describes heuristic and metaheuristic methods for treating the scheduling problem in an efficient manner. In addition, two test cases are presented in Section 4. The first uses simulation, while the second shows a real implementation of a production scheduling system. Finally, Section 5 presents some modeling strategies for building production scheduling systems. This book will be of interest to those working in the decision-making branches of production, in various operational research areas, as well as computational methods design. People from a diverse background ranging from academia and research to those working in industry, can take advantage of this volume

Balancing labor requirements in a manufacturing environment

“This research examines construction environments within manufacturing facilities, specifically semiconductor manufacturing facilities, and develops a new optimization method that is scalable for large construction projects with multiple execution modes and resource constraints. The model is developed to represent real-world conditions in which project activities do not have a fixed, prespecified duration but rather a total amount of work that is directly impacted by the level of resources assigned. To expand on the concept of resource driven project durations, this research aims to mimic manufacturing construction environments by allowing a non-continuous resource allocation to project tasks. This concept allows for resources to shift between projects in order to achieve the optimal result for the project manager. Our model generates a novel multi-objective resource constrained project scheduling problem. Specifically, two objectives are studied; the minimization of the total direct labor cost and the minimization of the resource leveling. This research will utilize multiple techniques to achieve resource leveling and discuss the advantage each one provides to the project team, as well as a comparison of the Pareto Fronts between the given resource leveling and cost minimization objective functions. Finally, a heuristic is developed utilizing partial linear relaxation to scale the optimization model for large scale projects. The computation results from multiple randomly generated case studies show that the new heuristic method is capable of generating high quality solutions at significantly less computational time”--Abstract, page iv

A Fuzzy Nonlinear Programming Approach for Optimizing the Performance of a Four-Objective Fluctuation Smoothing Rule in a Wafer Fabrication Factory

In theory, a scheduling problem can be formulated as a mathematical programming problem. In practice, dispatching rules are considered to be a more practical method of scheduling. However, the combination of mathematical programming and fuzzy dispatching rule has rarely been discussed in the literature. In this study, a fuzzy nonlinear programming (FNLP) approach is proposed for optimizing the scheduling performance of a four-factor fluctuation smoothing rule in a wafer fabrication factory. The proposed methodology considers the uncertainty in the remaining cycle time of a job and optimizes a fuzzy four-factor fluctuation-smoothing rule to sequence the jobs in front of each machine. The fuzzy four-factor fluctuation-smoothing rule has five adjustable parameters, the optimization of which results in an FNLP problem. The FNLP problem can be converted into an equivalent nonlinear programming (NLP) problem to be solved. The performance of the proposed methodology has been evaluated with a series of production simulation experiments; these experiments provide sufficient evidence to support the advantages of the proposed method over some existing scheduling methods