Query: Simulation based multiobjective schedule optimization in semiconductor manufacturing

Candidate papers:

abstract: In semiconductor manufacturing, it requires more than one objective such as cycle time, machine utilization and due date accuracy to be kept in focus simultaneously, while developing an effective scheduling. In this paper, a near optimal solution, which is not inferior to any other feasible solutions in terms of all objectives, is generated with a combination of the analytically optimal and simulation based scheduling approach. First, the job shop scheduling problem is modeled using the discrete event simulation approach and the problem is divided in to simulation clock based lot selection sub problems. Then, at each decision instant in simulated time, a Pareto optimal lot is selected using the various techniques to deal with multiobjective optimization such as weighted aggregation approach, global criterion method, minimum deviation method, and compromise programming. An illustration shows how these techniques work effectively in solving the multiobjective scheduling problem using discrete event simulation.
author_names: Amit Kumar Gupta

Appa Iyer Sivakumar
title: Simulation based multiobjective schedule optimization in semiconductor manufacturing
venue: Proceedings of the Winter Simulation Conference
year: 2002

abstract: This paper proposes a two stage joint optimization problem of green manufacturing and maintenance for semiconductor wafer (TSGMM SW) considering manufacturing stage, inspection, and repair stage simultaneously, which is a typical NP hard problem with practical research significance and value. Aiming at this problem, a green scheduling model with the objective of minimizing makespan, total carbon emissions, and total preventive maintenance (PM) costs is constructed, and an improved hybrid multiobjective multiverse optimization (IHMMVO) algorithm is proposed in this paper. The joint optimization of green manufacturing and maintenance is realized by designing synchronous scheduling and maintenance strategy for wafer manufacturing and equipment PM. The diversity of the population is expanded and the optimization performance of IHMMVO is improved by designing the initial population fusion strategy and subpopulation evolution strategy. In the experimental phase, we perform the simulation experiments of 900 test cases randomly generated from 90 parameter combinations. The IHMMVO algorithm is compared with other existing algorithms to verify the effectiveness and feasibility for TSGMM SW.
author_names: Jun Dong

Chunming Ye
title: Research on Two Stage Joint Optimization Problem of Green Manufacturing and Maintenance for Semiconductor Wafer
year: 2020

abstract: As an important and challenging problem, the scheduling of semiconductor manufacturing is a hot topic in both engineering and academic fields. Its purpose is to satisfy production constraints on both production process and resources, as well as optimizing some performance indexes like cycle time, movement, etc. However, due to its complexities, it is hard to describe the scheduling process with a mathematical model, or to use conventional methods to optimize its scheduling problem. A Simulation approach is proposed to optimize the scheduling of a semiconductor manufacturing system, i.e. a simulation based optimization (SBO) approach. Because the high computational cost of SBO approach could hinder its application in the real production line, a parallel/distributed architecture is discussed to improve its efficiency. Using genetic algorithm (GA) as an optimization algorithm, the proposed parallel SBO based scheduling approach for semiconductor manufacturing system is tested for its feasibility and effectiveness.
author_names: Yumin Ma

Fei Qiao

Wei Yu

Jianfeng Lu
title: Parallel simulation based optimization on scheduling of a semiconductor manufacturing system
venue: Proceedings of the Winter Simulation Conference 2014
year: 2014

abstract: As an important and challenging problem, the scheduling of semiconductor manufacturing is a hot topic in both engineering and academic field. Its purpose is to satisfy production constraints on time, cost and quality while optimizing some performance indexes like cycle time, movement, WIP and etc. However, due to complexities of semiconductor manufacturing system, conventional technologies and/or methods are hard to solve this kind of scheduling problem. A new scheduling approach based on simulation based optimization (SBO) is proposed in this paper. For the issue of the high computational cost including both CPU time and memory space which could hinder the application of SBO scheduling in practice, a distributed/parallel architecture is discussed. With genetic algorithm as an optimization algorithm, the proposed SBO based scheduling approach for semiconductor manufacturing system is tested on its feasibility and effectiveness.
author_names: Hai Gao

Fei Qiao

Yumin Ma

Ling-zhi Kong
title: A simulation based optimization approach for scheduling of a semiconductor manufacturing system
venue: 2014 IEEE International Conference on System Science and Engineering (ICSSE)
year: 2014

abstract: This article implements for the first time a numerical semiconductor device simulation based multiobjective evolutionary algorithm (MOEA) for the characteristic optimization of amorphous silicon thin film solar cells, based upon a unified optimization framework (UOF) To calculate the device's characteristic, a set of coupled solar cell transport equations consisting of the Poisson equation, the electron hole current continuity equations, and the photo generation model is solved numerically. Electrical characteristics, the short circuited current, the open circuited voltage, and the conversion efficiency are calculated to analyze the properties of the explored solar cells. The aforementioned device simulation results are used to evaluate the fitness score and access the evolutionary quality of designing parameters via the implemented non dominating sorting genetic algorithm (NSGA II) in the UOF. Notably, designing parameters including the material and structural parameters, and the doping concentrations are simultaneously optimized for the explored solar cells. The simulation based MOEA methodology is useful in optimal structure design and manufacturing of semiconductor solar cells.
author_names: Yiming Li

Yu-Yu Chen

Chieh-Yang Chen

Cheng-Han Shen

Hui-Wen Cheng

I-Hsiu Lo

Chun-Nan Chen
title: Device Simulation Based Multiobjective Evolutionary Algorithm for Process Optimization of Semiconductor Solar Cells
year: 2013

abstract: The increasing customization of products, which leads to greater variances and smaller lot sizes, requires highly flexible manufacturing systems. These systems are subject to dynamic influences and demand increasing effort for the generation of feasible production schedules and process control. This paper presents an approach for dealing with these challenges. First, production scheduling is executed by coupling an optimization heuristic with a simulation model. Second, real time system state data, to be provided by forthcoming cyber physical systems, is fed back, so that the simulation model is continuously updated and the optimization heuristic can either adjust an existing schedule or generate a new one. The potential of the approach was tested by means of a use case embracing a semiconductor manufacturing facility, in which the simulation results were employed to support the selection of better dispatching rules, improving flexible manufacturing systems performance regarding the average production cycle time.
author_names: Mirko Kuck

Jens Ehm

Torsten Hildebrandt

Michael J Freitag

Enzo Morosini Frazzon
title: Potential of data driven simulation based optimization for adaptive scheduling and control of dynamic manufacturing systems
venue: 2016 Winter Simulation Conference (WSC)
year: 2016

abstract: In semiconductor manufacturing we face many intricate scheduling problems. Simulation based scheduling is a promising approach to deal with them. In conjunction with a metaheuristic we can solve many problem instances in a satisfactory manner. Nevertheless the quality of the results varies across the range of diverse challenges. Instead of performing extensive tests to determine the best metaheuristic and the optimal parameter setting for each case we propose the use of hyper heuristics. A hyper heuristic manages multiple metaheuristics to generate a solution for a broad field of applications. This paper will introduce two hyper heuristics, one is an extended particle swarm algorithm the other is an integrated approach based on an evolutionary algorithm.
author_names: Tobias Uhlig

Falk Stefan Pappert

Oliver Rose
title: Simulation based optimization for semiconductor manufacturing using hyper heuristics
venue: WSC '12
year: 2012

abstract: Simulation based optimization is an established approach to handle complex scheduling problems. The problem examined in this study is scheduling jobs for groups of cluster tools in semiconductor manufacturing including a combination of sequencing, partitioning, and grouping of jobs with additional constraints. We use a specialized fast simulator to evaluate the generated schedules which allows us to run a large number of optimization iterations. For optimization we propose a simulated annealing algorithm to generate the schedules. It is implemented as a special instance of our adaptable evolutionary algorithm framework. As a consequence it is easy to adapt and extend the algorithm. For example, we can make use of various already existing problem representations that are geared to excel at certain aspects of our problem. Furthermore, we are able to parallelize the algorithm by using a population of optimization runs.
author_names: Tobias Uhlig

Oliver Rose
title: Simulation based optimization for groups of cluster tools in semiconductor manufacturing using simulated annealing
venue: Proceedings of the 2011 Winter Simulation Conference (WSC)
year: 2011

abstract: This paper presents initial results on the application of a simulation based Approximate Dynamic Programming (ADP) approach for the optimization of Preventive Maintenance (PM) scheduling decisions in semiconductor manufacturing systems. In particular, the so called Intel Mini Fab benchmark is used as an illustrative example. Our approach is based on an actor critic architecture in which the critic corresponds to a parametric estimation of the optimal differential cost for an infinite horizon average cost criterion based optimization model. The actor is defined using post decision state variables and a heuristic approach. Our algorithm also utilizes a temporal difference learning algorithm with a gradient descent approach to tune a linear parametric structure that approximates the optimal differential cost function. Simulation experiments validated the applicability of our algorithm in the Intel Mini Fab by showing a significant reduction in average cycle time when compared with a series of fixed baseline PM schedules.
author_names: Jose A Ramirez-Hernandez

Emmanuel Fernandez
title: Optimization of Preventive Maintenance scheduling in semiconductor manufacturing models using a simulation based Approximate Dynamic Programming approach
venue: 49th IEEE Conference on Decision and Control (CDC)
year: 2010

abstract: Semiconductor manufacturing fab is one of the most sophisticated man made system, consisting of hundreds of very expensive equipment connected by highly automated material handling system. Operation schedule has huge impact on the productivity of the fab. Obtaining efficient schedule for numerous equipment is a very complex problem, which cannot be solved by conventional optimization techniques. Hence, heuristic dispatching rules combined with fab simulation is often used for generating fab operation schedule. In this paper, we formulate the fab scheduling problem as a semi Markov decision process and propose a reinforcement learning method used in conjunction with the fab simulator to obtain the (near )optimal dispatching policy. Resulting schedule obtained by the proposed method shows better performance than heuristic rules whose parameters are tuned by human experts.
author_names: Won-jun Lee

Byung-Hee Kim

Keyhoon Ko

Hayong Shin
title: Simulation Based Multi Objective Fab Scheduling by Using Reinforcement Learning
venue: 2019 Winter Simulation Conference (WSC)
year: 2019