A Simulation of composite dispatching rules, CONWIP and push lot release in semiconductor fabrication

This paper evaluates dispatching rules and order release policies in two fabs representing two wafer fabrication modes, namely, ASIC and low-mix high-volume production. Order release policies were fixed-interval (push) release, and constant work-in-process, CONWIP (pull) policy. Following rigorous fab modeling and statistical analysis, new composite dispatching rules were found to be robust for system cycle time and due-date adherence measures, in both production modes

Cycle Time Analysis For Photolithography Tools In Semiconductor Manufacturing Industry With Simulation Model : A Case Study [TR940. S618 2008 f rb].

Perkembangan industri semikonduktor dalam bidang fabrikasi biasanya melibatkan kos pelaburan yang tinggi terutamanya dalam alatan photolithography. The industry of semiconductor wafer fabrication (“fab”) has invested a huge amount of capital on the manufacturing equipments particular in photolithograph

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

Development and Simulation Assessment of Semiconductor Production System Enhancements for Fast Cycle Times

Long cycle times in semiconductor manufacturing represent an increasing challenge for the industry and lead to a growing need of break-through approaches to reduce it. Small lot sizes and the conversion of batch processes to mini-batch or single-wafer processes are widely regarded as a promising means for a step-wise cycle time reduction. Our analysis with discrete-event simulation and queueing theory shows that small lot size and the replacement of batch tools with mini-batch or single wafer tools are beneficial but lot size reduction lacks persuasive effectiveness if reduced by more than half. Because the results are not completely convincing, we develop a new semiconductor tool type that further reduces cycle time by lot streaming leveraging the lot size reduction efforts. We show that this combined approach can lead to a cycle time reduction of more than 80%

Intelligent production control for time-constrained complex job shops

Im Zuge der zunehmenden Komplexität der Produktion wird der Wunsch nach einer intelligenten Steuerung der Abläufe in der Fertigung immer größer. Sogenannte Complex Job Shops bezeichnen dabei die komplexesten Produktionsumgebungen, die deshalb ein hohes Maß an Agilität in der Steuerung erfordern. Unter diesen Umgebungen sticht die besonders Halbleiterfertigung hervor, da sie alle Komplexitäten eines Complex Job-Shop vereint. Deshalb ist die operative Exzellenz der Schlüssel zum Erfolg in der Halbleiterindustrie. Diese Exzellenz hängt ganz entscheidend von einer intelligenten Produktionssteuerung ab. Ein Hauptproblem bei der Steuerung solcher Complex Job-Shops, in diesem Fall der Halbleiterfertigung, ist das Vorhandensein von Zeitbeschränkungen (sog. time-constraints), die die Transitionszeit von Produkten zwischen zwei, meist aufeinanderfolgenden, Prozessen begrenzen. Die Einhaltung dieser produktspezifischen Zeitvorgaben ist von größter Bedeutung, da Verstöße zum Verlust des betreffenden Produkts führen. Der Stand der Technik bei der Produktionssteuerung dieser Dispositionsentscheidungen, die auf die Einhaltung der Zeitvorgaben abzielen, basiert auf einer fehleranfälligen und für die Mitarbeiter belastenden manuellen Steuerung. In dieser Arbeit wird daher ein neuartiger, echtzeitdatenbasierter Ansatz zur intelligenten Steuerung der Produktionssteuerung für time-constrained Complex Job Shops vorgestellt. Unter Verwendung einer jederzeit aktuellen Replikation des realen Systems werden sowohl je ein uni-, multivariates Zeitreihenmodell als auch ein digitaler Zwilling genutzt, um Vorhersagen über die Verletzung dieser time-constraints zu erhalten. In einem zweiten Schritt wird auf der Grundlage der Erwartung von Zeitüberschreitungen die Produktionssteuerung abgeleitet und mit Echtzeitdaten anhand eines realen Halbleiterwerks implementiert. Der daraus resultierende Ansatz wird gemeinsam mit dem Stand der Technik validiert und zeigt signifikante Verbesserungen, da viele Verletzungen von time-constraints verhindert werden können. Zukünftig soll die intelligente Produktionssteuerung daher in weiteren Complex Job Shop-Umgebungen evaluiert und ausgerollt werden

Artificial Neural Networks in Production Scheduling and Yield Prediction of Semiconductor Wafer Fabrication System

With the development of artificial intelligence, the artificial neural networks (ANN) are widely used in the control, decision‐making and prediction of complex discrete event manufacturing systems. Wafer fabrication is one of the most complicated and high competence manufacturing phases. The production scheduling and yield prediction are two critical issues in the operation of semiconductor wafer fabrication system (SWFS). This chapter proposed two fuzzy neural networks for the production rescheduling strategy decision and the die yield prediction. Firstly, a fuzzy neural network (FNN)‐based rescheduling decision model is implemented, which can rapidly choose an optimized rescheduling strategy to schedule the semiconductor wafer fabrication lines according to the current system disturbances. The experimental results demonstrate the effectiveness of proposed FNN‐based rescheduling decision mechanism approach over the alternatives (back‐propagation neural network and Multivariate regression). Secondly, a novel fuzzy neural network‐based yield prediction model is proposed to improve prediction accuracy of die yield in which the impact factors of yield and critical electrical test parameters are considered simultaneously and are taken as independent variables. The comparison experiment verifies the proposed yield prediction method improves on three traditional yield prediction methods with respect to prediction accuracy

Cycle Time Analysis For Photolithography Tools In Semiconductor Manufacturing Industry With Simulation Model: A Case Study

Perkembangan industri semikonduktor dalam bidang fabrikasi biasanya melibatkan kos pelaburan yang tinggi terutamanya dalam alatan photolithography. Perkembangan pesat dalam bidang industri semikonduktor kini telah memerangsangkan teknik untuk mengoptimumkan penggunaan mesin-mesin dengan efektif setelah membelanjakan beribu juta dalam perlaburan. Tanpa penggunaan perisian komputer yang canggih dalam analisis, adalah sukar untuk menggunakan teknik purba dalam analisis pengiraan apabila menghadapi perkembangan produk yang semakin tinggi teknologinya. Dalam kajian ini, satu model simulasi telah dibina untuk menganalisis masa mendulu dalam alatan photolithography melalui teknik yang lebih sistematik dan efektif. Model simulasi ini telah dibina berasaskan perisian computer yang memerlukan informasi yang teliti seperti mas a memproses dan juga aliran proses dalam alatan photolithography. The industry of semiconductor wafer fabrication ("fab") has invested a huge amount of capital on the manufacturing equipments particular in photolithography area which has driven the needs to re-look at the most profitable way of utilizing and operating them efficiently. Traditional industrial engineering analysis techniques through mathematical models or static models for the studies of photolithography process are simply not adequate to analyze these complex environments. In this research, a more realistic representation of photolithography tools that can give a better prediction results and a more systematic methodology for minimizing photolithography cycle time is presented. The proposed method is to reduce waiting time and increase utilization of the photolithography process, which would result in an overall equipment cycle time reduction

The Role of Skill Upgrading in Manufacturing Performance

This section examines the industry-wide trend of upgrading the skills of production workers in the semiconductor industry. This analysis discusses the industry characteristics driving this trend, the human resource policies that support skill upgrades, and the payoffs associated with such upgrades. To provide a deeper understanding of the process of skill upgrading, two fabs from our sample are analyzed in detail. One fab is located in Japan (pseudonym Jfab) and the other fab is located in the U.S. (pseudonym USfab). As a central part of their manufacturing strategy, these fabs have emphasized the upskilling of operators particularly for equipment maintenance activities. Through human resource policies, both fabs have extended the breadth and depth of their employees\u27 skills (our definition of skill upgrading), particularly the skills of their operators and technicians. At the time of our visit, a manager at Jfab estimated that they were 95% self-sufficient in maintaining their own equipment rather than using the vendor. He explained, We don\u27t use vendor maintenance because it is very expensive and because our people are better at it than the vendors\u27 personnel. We end up teaching the employees of the vendors about their own equipment! A manager at USfab echoed these sentiments regarding vendors: Contracts are expensive and we can do better. USfab also has concentrated on upgrading the skills of its operators while it merged the operator and technician occupations into a production specialist position (a pseudonym). USfab\u27s production specialists now perform 90% of the basic preventative maintenance (e.g., daily checks, chamber cleans, PMs). For equipment maintenance tasks, these two fabs have substituted participation by line workers for engineering time. Their operators rank at the top of our fifteen fab sample in terms of their intensity of participation in equipment maintenance activities, while their equipment engineers rank in the middle. In addition to equipment maintenance, another set of activities that affects manufacturing performance focuses on process-related problems and the manufacturing precision of the equipment. These activities can be grouped under the umbrella of statistical process control (SPC), which requires personnel to compare measures of processing outcomes (e.g., the height of a layer, the accuracy of alignment, processing time, particle generation,) against detailed specifications set by the process development group. For statistical process control (SPC) duties, Jfab and USfab do not emphasize the role of their line workers. Instead, Jfab has emphasized the role of the process engineer in conducting SPC, and its process engineers rank at the top of our fifteen fab sample in terms of the intensity with which they use SPC. Process engineers at USfab fall towards the bottom of our SPC rankings, and the fab\u27s SPC capabilities are rudimentary with no automated SPC capabilities and no real time process adjustment. Engineers at USfab were plagued with fire-fighting responsibilities, since they committed approximately 80% of their time to fire-fighting. The engineers were anxious for the program of skill upgrading of operators to bear fruit so that operators could assume more trouble-shooting responsibilities. As one engineer put it, We spend all of our time [taking care of] lots that went on hold. We want to train other people to do this, so we can have time for [more training and projects]. These findings suggest that both companies rely on their line workers for equipment performance, but not for process control. Jfab relies much more heavily on its process engineers, while USfab lags behind in establishing a focus for its process control efforts. As described below, the two companies in this case study have established human resource policies consistent with deepening and broadening the skills of their manufacturing personnel in order to pursue their strategies for equipment maintenance and SPC. They have, however, experienced very different levels of success measured by our five manufacturing metrics (stepper throughput, cycle time, direct labor productivity, line yield, and defect density). Jfab scores consistently at the top of the fifteen fabs in our sample while USfab scores in the bottom half. Their divergent performance can be at least partially attributed to the different level of stability of their production environments. Jfab was operating in a relatively stable environment with few process flows and moderated process problems with an advanced SPC capability. In contrast, USfab was undergoing a reorganization of its operations, new process introductions, and adopting a new shop-floor work organization to better integrate its upskilled production specialists into problemsolving activities. We anticipate that with time, USfab will at least partially catch up to the level of manufacturing performance enjoyed by Jfab, as their aggressive skill upgrade program matures, and as they adjust to the changes to their organizational structure and manufacturing process technologies. This section concludes by considering the influences of automation and differences in employment systems on the pervasiveness of skill upgrade efforts across job categories