Scheduling of Batch Processors in Semiconductor Manufacturing – A Review

In this paper a review on scheduling of batch processors (SBP) in semiconductor manufacturing (SM) is presented. It classifies SBP in SM into 12 groups. The suggested classification scheme organizes the SBP in SM literature, summarizes the current research results for different problem types. The classification results are presented based on various distributions and various methodologies applied for SBP in SM are briefly highlighted. A comprehensive list of references is presented. It is hoped that, this review will provide a source for other researchers/readers interested in SBP in SM research and help simulate further interest.Singapore-MIT Alliance (SMA

Evaluation and extension of threaded control for high-mix semiconductor manufacturing

textIn the recent years threaded run-to-run (RtR) control algorithms have experienced drawbacks under certain circumstances, one such trait is when applied to high-mix of products such as in Application Specific Integrated Circuits (ASIC) foundries. The variations in the process are a function of the product being manufactured as well as the tool being used. The presence of semiconductor layers increases the number of times the lithography process must be repeated. Successive layers having different patterns must be exposed using different reticles/masks in order to maximize tool utilizations. The objectives of this research are to develop a set of methodologies for evaluation and extension of threaded control applied to overlay. This project defines methods to quantify the efficacy of threaded controls, finds the drawbacks of threaded control under production of high mix of semiconductors and suggests extensions and alternatives to improve threaded control. To evaluate the performance of threaded control, extensive simulations were performed in MATLAB. The effects of noise, disturbances, sampling and delays on the control and estimation performance of threaded controller were studied through these simulations. Based on the results obtained, several ideas to extend threaded control by reducing overall number of threads, by improving thread definitions and combination have been introduced. A unique idea of sampling the measurements dynamically based on the estimation accuracy is also presented. Future work includes implementing the extensions to threaded control suggested in this work in real production data and comparing the results without the use of those methods. Future work also includes building new alternatives to threaded control.Electrical and Computer Engineerin

Capacity requirement planning master data solution procurement at Qimonda Portugal SA

Estágio realizado na Qimonda Portugal S. A. e orientado pelo Eng.º Peter MaderaTese de mestrado integrado. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 200

Simulation and optimization techniques applied in semiconductor assembly and test operations

The importance of back-end operations in semiconductor manufacturing has been growing steadily in the face of higher customer expectations and stronger competition in the industry. In order to achieve low cycle times, high throughput, and high utilization while improving due-date performance, more effective tools are needed to support machine setup and lot dispatching decisions. In previous work, the problem of maximizing the weighted throughput of lots undergoing assembly and test (AT), while ensuring that critical lots are given priority, was investigated and a greedy randomized adaptive search procedure (GRASP) developed to find solutions. Optimization techniques have long been used for scheduling manufacturing operations on a daily basis. Solutions provide a prescription for machine setups and job processing over a finite the planning horizon. In contrast, simulation provides more detail but in a normative sense. It tells you how the system will evolve in real time for a given demand, a given set of resources and rules for using them. A simulation model can also accommodate changeovers, initial setups and multi-pass requirements easily. The first part of the research is to show how the results of an optimization model can be integrated with the decisions made within a simulation model. The problem addressed is defined in terms of four hierarchical objectives: minimize the weighted sum of key device shortages, maximize weighted throughput, minimize the number of machines used, and minimize makespan for a given set of lots in queue, and a set of resources that includes machines and tooling. The facility can be viewed as a reentrant flow shop. The basic simulation was written in AutoSched AP (ASAP) and then enhanced with the help of customization features available in the software. Several new dispatch rules were developed. Rule_First_setup is able to initialize the simulation with the setups obtained with the GRASP. Rule_All_setups enables a machine to select the setup provided by the optimization solution whenever a decision is about to be made on which setup to choose subsequent to the initial setup. Rule_Hotlot was also proposed to prioritize the processing of the hot lots that contain key devices. The objective of the second part of the research is to design and implement heuristics within the simulation model to schedule back-end operations in a semiconductor AT facility. Rule_Setupnum lets the machines determine which key device to process according to a machine setup frequency table constructed from the GRASP solution. GRASP_asap embeds a more robust selection features of GRASP in the ASAP model through customization. This allows ASAP to explore a larger portion of the feasible region at each decision point by randomizing machine setups using adaptive probability distributions that are a function of solution quality. Rule_Greedy, which is a simplification of GRASP_asap, always picks the setup for a particular machine that gives the greatest marginal improvement in the objective function among all candidates. The purpose of the third part of the research is to statistically validate the relative effectiveness of our top six dispatch rules by comparing their performance on 30 real and randomly generated data sets. Using both GRASP and our ASAP discrete event simulation model, we have (1) identified the general order of dispatch rule performance, (2) investigated the impact of having setups installed on machines at time zero on rule performance, (3) determined the conditions under which restricting the maximum number of changeover affects the rule performance, and (4) studied the factors that might simultaneously affect rule performance with the help of a common random numbers experimental design. In the analysis, the first two objectives, weighted key device shortages and weighted throughput, are used to measure outcomes.Operations Research and Industrial Engineerin

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

Analyzing Controllable Factors Influencing Cycle Time Distribution in Semiconductor Industries

abstract: Semiconductor manufacturing is one of the most complex manufacturing systems in today’s times. Since semiconductor industry is extremely consumer driven, market demands within this industry change rapidly. It is therefore very crucial for these industries to be able to predict cycle time very accurately in order to quote accurate delivery dates. Discrete Event Simulation (DES) models are often used to model these complex manufacturing systems in order to generate estimates of the cycle time distribution. However, building models and executing them consumes sufficient time and resources. The objective of this research is to determine the influence of input parameters on the cycle time distribution of a semiconductor or high volume electronics manufacturing system. This will help the decision makers to implement system changes to improve the predictability of their cycle time distribution without having to run simulation models. In order to understand how input parameters impact the cycle time, Design of Experiments (DOE) is performed. The response variables considered are the attributes of cycle time distribution which include the four moments and percentiles. The input to this DOE is the output from the simulation runs. Main effects, two-way and three-way interactions for these input variables are analyzed. The implications of these results to real world scenarios are explained which would help manufactures understand the effects of the interactions between the input factors on the estimates of cycle time distribution. The shape of the cycle time distributions is different for different types of systems. Also, DES requires substantial resources and time to run. In an effort to generalize the results obtained in semiconductor manufacturing analysis, a non- complex system is considered.Dissertation/ThesisMasters Thesis Mechanical Engineering 201

A dynamic simulation framework for biopharmaceutical capacity management

In biopharmaceutical manufacturing there have been significant increases in drug complexity, risk of clinical failure, regulatory pressures and demand. Compounded with the rise in competition and pressures of maintaining high profit margins this means that manufacturers have to produce more efficient and lower capital intensive processes. More are opting to use simulation tools to perform such revisions and to experiment with various process alternatives, activities which would be time consuming and expensive to carry out within the real system. A review of existing models created for different biopharmaceutical activities using the Extend® (ImagineThat!, CA) platform led to the development of a standard framework to guide the design and construct of a more efficient model. The premise of the framework was that any ‘good’ model should meet five requirement specifications: 1) Intuitive to the user, 2) Short Run-Time, 3) Short Development Time, 4) Relevant and has Ease of Data Input/Output, and 5) Maximised Reusability and Sustainability. Three different case studies were used to test the framework, two biotechnology manufacturing and one fill/finish, with each adding a new layer of understanding and depth to the standard due to the challenges faced. These Included procedures and constraints related to complex resource allocation, multi-product scheduling and complex ‘lookahead’ logic for scheduling activities such as buffer makeup and difficulties surrounding data availability. Subsequently, in order to review the relevance of the models, various analyses were carried out including schedule optimisation, debottlenecking and Monte Carlo simulations, using various data representation tools to deterministically and stochastically answer the different questions within each case study scope. The work in this thesis demonstrated the benefits of using the developed standard as an aid to building decision-making tools for biopharmaceutical manufacturing capacity management, so as to increase the quality and efficiency of decision making to produce less capital intensive processes

Semantic data integration for supply chain management: with a specific focus on applications in the semiconductor industry

Supply Chain Management (SCM) is essential to monitor, control, and enhance the performance of SCs. Increasing globalization and diversity of Supply Chains (SC)s lead to complex SC structures, limited visibility among SC partners, and challenging collaboration caused by dispersed data silos. Digitalization is responsible for driving and transforming SCs of fundamental sectors such as the semiconductor industry. This is further accelerated due to the inevitable role that semiconductor products play in electronics, IoT, and security systems. Semiconductor SCM is unique as the SC operations exhibit special features, e.g., long production lead times and short product life. Hence, systematic SCM is required to establish information exchange, overcome inefficiency resulting from incompatibility, and adapt to industry-specific challenges. The Semantic Web is designed for linking data and establishing information exchange. Semantic models provide high-level descriptions of the domain that enable interoperability. Semantic data integration consolidates the heterogeneous data into meaningful and valuable information. The main goal of this thesis is to investigate Semantic Web Technologies (SWT) for SCM with a specific focus on applications in the semiconductor industry. As part of SCM, End-to-End SC modeling ensures visibility of SC partners and flows. Existing models are limited in the way they represent operational SC relationships beyond one-to-one structures. The scarcity of empirical data from multiple SC partners hinders the analysis of the impact of supply network partners on each other and the benchmarking of the overall SC performance. In our work, we investigate (i) how semantic models can be used to standardize and benchmark SCs. Moreover, in a volatile and unpredictable environment, SC experts require methodical and efficient approaches to integrate various data sources for informed decision-making regarding SC behavior. Thus, this work addresses (ii) how semantic data integration can help make SCs more efficient and resilient. Moreover, to secure a good position in a competitive market, semiconductor SCs strive to implement operational strategies to control demand variation, i.e., bullwhip, while maintaining sustainable relationships with customers. We examine (iii) how we can apply semantic technologies to specifically support semiconductor SCs. In this thesis, we provide semantic models that integrate, in a standardized way, SC processes, structure, and flows, ensuring both an elaborate understanding of the holistic SCs and including granular operational details. We demonstrate that these models enable the instantiation of a synthetic SC for benchmarking. We contribute with semantic data integration applications to enable interoperability and make SCs more efficient and resilient. Moreover, we leverage ontologies and KGs to implement customer-oriented bullwhip-taming strategies. We create semantic-based approaches intertwined with Artificial Intelligence (AI) algorithms to address semiconductor industry specifics and ensure operational excellence. The results prove that relying on semantic technologies contributes to achieving rigorous and systematic SCM. We deem that better standardization, simulation, benchmarking, and analysis, as elaborated in the contributions, will help master more complex SC scenarios. SCs stakeholders can increasingly understand the domain and thus are better equipped with effective control strategies to restrain disruption accelerators, such as the bullwhip effect. In essence, the proposed Sematic Web Technology-based strategies unlock the potential to increase the efficiency, resilience, and operational excellence of supply networks and the semiconductor SC in particular