Cycle Time Estimation in a Semiconductor Wafer Fab: A concatenated Machine Learning Approach

Die fortschreitende Digitalisierung aller Bereiche des Lebens und der Industrie lässt die Nachfrage nach Mikrochips steigen. Immer mehr Branchen – unter anderem auch die Automobilindustrie – stellen fest, dass die Lieferketten heutzutage von den Halbleiterherstellern abhängig sind, was kürzlich zur Halbleiterkrise geführt hat. Diese Situation erhöht den Bedarf an genauen Vorhersagen von Lieferzeiten von Halbleitern. Da aber deren Produktion extrem schwierig ist, sind solche Schätzungen nicht einfach zu erstellen. Gängige Ansätze sind entweder zu simpel (z.B. Mittelwert- oder rollierende Mittelwertschätzer) oder benötigen zu viel Zeit für detaillierte Szenarioanalysen (z.B. ereignisdiskrete Simulationen). Daher wird in dieser Arbeit eine neue Methodik vorgeschlagen, die genauer als Mittelwert- oder rollierende Mittelwertschätzer, aber schneller als Simulationen sein soll. Diese Methodik nutzt eine Verkettung von Modellen des maschinellen Lernens, die in der Lage sind, Wartezeiten in einer Halbleiterfabrik auf der Grundlage einer Reihe von Merkmalen vorherzusagen. In dieser Arbeit wird diese Methodik entwickelt und analysiert. Sie umfasst eine detaillierte Analyse der für jedes Modell benötigten Merkmale, eine Analyse des genauen Produktionsprozesses, den jedes Produkt durchlaufen muss – was als "Route" bezeichnet wird – und entwickelte Strategien zur Bewältigung von Unsicherheiten, wenn die Merkmalswerte in der Zukunft nicht bekannt sind. Zusätzlichwird die vorgeschlagene Methodik mit realen Betriebsdaten aus einerWafer-Fabrik der Robert Bosch GmbH evaluiert. Es kann gezeigt werden, dass die Methodik den Mittelwert- und Rollierenden Mittelwertschätzern überlegen ist, insbesondere in Situationen, in denen die Zykluszeit eines Loses signifikant vom Mittelwert abweicht. Zusätzlich kann gezeigt werden, dass die Ausführungszeit der Methode signifikant kürzer ist als die einer detaillierten Simulation

MODELING AND SIMULATION OF A SEMICONDUCTOR MANUFACTURING FAB FOR CYCLE TIME ANALYSIS

The goal of the thesis is to conduct a study of the effects of scheduling policies and machine failures on the manufacturing cycle time of the Integrated Circuit (IC) manufacturing process for two processor chips, namely Skylake and Kabylake, manufactured by Intel. The fab simulation model was developed as First in First Out (FIFO), Shortest Processing Time (SPT), Priority based (PB), and Failure FIFO (machine failures) model, and the average cycle times and queue waiting times under the four scheduling policy models were compared for both the Skylake and Kabylake wafers. The study revealed that scheduling policies SPT and PB increased the average cycle time for Skylake wafers while decreasing the average cycle time for the Kabylake wafers, when compared to the base FIFO model. Machine failures increased the average cycle time for both types of wafers

A framework for generating operational characteristic curves for semiconductor manufacturing systems using flexible and reusable discrete event simulations

This thesis proposes a framework for generating operating curves for semiconductor manufacturing facilities using a modular flexible discrete event simulation (DES) model embedded in an application that automates the design of experiments for the simulations. Typically, operating curves are generated using analytical queueing models that are difficult to implement and hence, can only be used for benchmarking purposes. Alternatively, DES models are more capable of capturing the complexities of a semiconductor manufacturing facility such as re-entrancy, rework and non-identical toolsets. However, traditional craft-based simulations require much time and resources. The proposed methodology aims to reduce this time by automatically calculating the parameters for experimentation and generating the simulation model. It proposes a novel method to more appropriately allocate simulation effort by selecting design points more relevant to the operating curve. The methodology was initially applied to a single toolset model and tested as a pilot case study using actual factory data. Overall, the resulting operating curves matched that of the actual data. Subsequently, the methodology was applied to a full semiconductor manufacturing facility, using datasets from the Semiconductor Wafer Manufacturing Data Format Specification. The automated framework was shown to generate the curves rapidly and comparisons against a number of queueing model equivalents showed that the DES curves were more accurate. The implications of this work mean that on deployment of the application, semiconductor manufacturers can quickly obtain an accurate operating curve of their factory that could be used to aid in capacity planning and enable better decision-making regarding allocation of resources

A Kriging Method for Modeling Cycle Time-Throughput Profiles in Manufacturing

In semiconductor manufacturing, the steady-state behavior of a wafer fab system can be characterized by its cycle time-throughput profiles. These profiles quantify the relationship between the cycle time of a product and the system throughput and product mix. The objective of this work is to efficiently generate such cycle time-throughput profiles in manufacturing which can further assist decision makings in production planning.;In this research, a metamodeling approach based on Stochastic Kriging model with Qualitative factors (SKQ) has been adopted to quantify the target relationship of interest. Furthermore, a sequential experimental design procedure is developed to improve the efficiency of simulation experiments. For the initial design, a Sequential Conditional Maximin algorithm is utilized. Regarding the follow-up designs, batches of design points are determined using a Particle Swarm Optimization algorithm.;The procedure is applied to a Jackson network, as well as a scale-down wafer fab system. In both examples, the prediction performance of the SKQ model is promising. It is also shown that the SKQ model provides narrower confidence intervals compared to the Stochastic Kriging model (SK) by pooling the information of the qualitative variables

Optimization of in-line semiconductor measurement rates : balancing cost and risk in a high mix, low volume environment

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2004.Includes bibliographical references (p. 99-101).Due to a number of market development over the last decade, semiconductor manufacturing companies, including Intel Corporation, have added significant numbers of primarily high growth rate, high-mix, low-volume (HMLV) products to their portfolios. The rapid transition from high-volume manufacturing (HVM) to HMLV manufacturing has caused significant problems. Foremost, the needs of many HMLV customers are different from HVM customers and require different operational tradeoffs. Moreover, many of the HVM focused metrics, tools, systems and processes have proven ill-suited for managing the added complexities and more varied needs of HMLV customers. This thesis examines many of the problems caused by introducing HMLV products into an HVM wafer fabrication facility (commonly referred to as a fab), and explores potential solutions such as improved cultural and organizational alignment; capacity management and setup elimination; and scheduling and work-in-process management to name a few. Although the discussion focuses on semiconductor operations, the concepts easily generalize to other companies struggling with achieving operational excellence (OpX) in an HMLV environment. In addition to exploring the macroscopic HMLV issues, we also feature an in-depth analysis of one aspect of achieving OpX in the HMLV environment: the optimization of in-line metrology skip rates. Based on a review of the current methods, a new approach is suggested based on a Bayesian economic skip-lot model we call MOST/2. In general, MOST/2 suggests that significant cost savings can be realized with only modest increases in the material at risk per excursion if measurement rates are further reduced. Compared with the other methods analyzed, the data indicates that MOST/2(cont.) provides superior cost/risk balanced results. For the 27 operations analyzed, results include annual costs savings of over $95,000, cycle time savings of over 5.3 hours per lot, operator savings of over 4.2 people per year and metrology capacity utilization rate reductions of over 65%. Finally, a brief organizational study was conducted to identify political, cultural and strategic design changes that would bolster long-term operational excellence (OpX) in the HMLV environment. Suggested changes include better identification of customer needs, improved communication and linking between groups, modification and alignment of factory and performance metrics and the creation of a stand-alone HMLV organization.by Christopher R. Pandolfo.S.M.M.B.A

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

Control-friendly scheduling algorithms for multi-tool, multi-product manufacturing systems

textThe fabrication of semiconductor devices is a highly competitive and capital intensive industry. Due to the high costs of building wafer fabrication facilities (fabs), it is expected that products should be made efficiently with respect to both time and material, and that expensive unit operations (tools) should be utilized as much as possible. The process flow is characterized by frequent machine failures, drifting tool states, parallel processing, and reentrant flows. In addition, the competitive nature of the industry requires products to be made quickly and within tight tolerances. All of these factors conspire to make both the scheduling of product flow through the system and the control of product quality metrics extremely difficult. Up to now, much research has been done on the two problems separately, but until recently, interactions between the two systems, which can sometimes be detrimental to one another, have mostly been ignored. The research contained here seeks to tackle the scheduling problem by utilizing objectives based on control system parameters in order that the two systems might behave in a more beneficial manner. A non-threaded control system is used that models the multi-tool, multi-product process in a state space form, and estimates the states using a Kalman filter. Additionally, the process flow is modeled by a discrete event simulation. The two systems are then merged to give a representation of the overall system. Two control system matrices, the estimate error covariance matrix from the Kalman filter and a square form of the system observability matrix called the information matrix, are used to generate several control-based scheduling algorithms. These methods are then tested against more tradition approaches from the scheduling literature to determine their effectiveness on both the basis of how well they maintain the outputs near their targets and how well they minimize the cycle time of the products in the system. The two metrics are viewed simultaneously through use of Pareto plots and merits of the various scheduling methods are judged on the basis of Pareto optimality for several test cases.Chemical Engineerin

APPROXIMATE ANALYSIS OF RE-ENTRANT LINES WITH BERNOULLI RELIABILITY MODELS

Re-entrant lines are widely used in many manufacturing systems, such as semiconductor, electronics, etc. However, the performance analysis of re-entrant lines is largely unexplored due to its complexity. In this thesis, we present iterative procedures to approximate the production rate of re-entrant lines with Bernoulli reliability of machines. The convergence of the algorithms, uniqueness of the solution, and structural properties, have been proved analytically. The accuracy of the procedures is investigated numerically. It is shown that the approaches developed can either provide a lower bound or a closed estimate of the system production rate. Finally, a case study of automotive ignition component line with re-entrant washing operations is introduced to illustrate the applicability of the method. The results of this study suggest a possible route for modeling and analysis of re-entrant systems

OPTIMAL PREVENTIVE MAINTENANCE POLICIES FOR UNRELIABLE QUEUEING AND PRODUCTION SYSTEMS

Preventive Maintenance (PM) models have traditionally concentrated on utilizing machine ``technical" state information such as the degree of deterioration. However, in real manufacturing systems, additional system operational information such as work-in-process (WIP) inventory levels critically impact actual PM decisions. Surprisingly, the literature on models incorporating this important aspect is relatively sparse. This thesis attempts to fill some of the research gaps in this area by considering problems of optimal preventive maintenance explicitly under the context of unreliable queueing and production-inventory systems. We propose a two-level hierarchical modeling framework for PM planning and scheduling problems. In the higher level, our objective is to characterize structure of optimal PM policies. We start with a simple case in which queueing is not taken into account in the model. We show that a randomized PM policy, like the widely used ``time-window" policy in industry, is in general not optimal. We then consider the problem of optimal PM policies for an M/G/1 queueing system with an unreliable server. The decision problem is formulated as a semi-Markov decision process. We establish some structural properties, e.g., ``control-limit" type structure, that optimal policies will satisfy. We then take the optimal PM problem a step further by considering optimal joint PM and production control policies for unreliable production-inventory systems with time-dependent or operation-dependent failures. We show the optimal joint policies retain the ``control-limit" type structure in terms of the PM portion of the policy. For the production portion of the policy, some properties are also derived, but numerical studies show that in general optimal policies have more complicated structure than the simple control-limit form. The last part of the thesis is devoted to the lower level of the framework where the objective is to optimally schedule multiple PM tasks across a group of tools. We take into account information such as interdependence of PM tasks, WIP data and resource constraints, and formulate the problem as a mixed-integer program. Results of a simulation study comparing the performance of the model-based PM schedule with that of a baseline reference schedule are presented to illustrate the fitness of our solutions