Optimized Management of Controls in Semiconductor Manufacturing

International audienceIn order to minimize yield losses due to excursions, when a process or a tool shifts out of specifications, an algorithm is proposed to reduce the scope of analysis and provide in real time the number of lots po-tentially impacted. The algorithm is based on a Permanent Index per Context (IPC). The IPC allows a very large amount of data to be managed and helps to compute global risk indicators on production. The information provided by the IPC allows for the quick quantification of the potential loss in the production, and the identification of the set of production tools most likely to be the source of the excursion and the set of lots potentially impacted. A prototype has been developed for the defectivity workshop. Results show that the time of analysis can be strongly reduced and the average cycle time improved

Auto Defect Classification (ADC) Value for Patterned Wafer Inspection Systems in PLY Within a High Volume Wafer Manufacturing Fabrication Facility

The purpose of this investigation is to demonstrate value for Auto Defect Classification (ADC) for patterned wafer inspection systems within a high volume manufacturing fabrication in the Process Limited Yield (PLY) defect area. Process excursions in all functional Unit Process (UP) areas, examples are of etch, litho, diffusion, are monitored by PLY. Troubleshooting of process excursions using added defect density count with a small percentage (random or largest 50 examples) of and inline Scanning Electron Microscope (SEM) data classification review does not give a clear indication of the full wafer data. Statistical Process Control (SPC) trigging on total counts or defect density is not as powerful as making excursion decisions on classified data from ADC (Fisher, 2002). The ADC data gives classification of the entire wafer rather than a smaller sample making signature analysis to be an additional troubleshooting tool. The inline ADC data does not have near the resolution of the SEM but can be used to help make important decisions to what is occurring in the manufacturing line. The interest is to gain a full understanding of the current capabilities and limitation of ADC and to apply the learning to enable faster reaction and visibility into process and tool excursions within a high volume manufacturing fabrication. The Technical Learning Vehicle (TLV), high running product layer at the leading design rule, there were approximately 10,000 wafers a week with 1000 wafer die (chips) per wafer. A sustained improvement in yield of 1% across the entire manufacturing line would equate to almost 1 million dollars a month of saving. With the ability to tightly control multiple etch process tools, the resulting yield improvement was 3% across 15% of the line. With the baseline yield improvement along with ability to react quickly to process excursions, the combined improvement resulted in excessive of 5 million dollar a year of reoccurring savings

Variation reduction in a wafer fabrication line through inspection optimization

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 1997, and Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1997.Includes bibliographical references (p. 44).by John W. Bean.M.S

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 review of data mining applications in semiconductor manufacturing

The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI).For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.publishersversionpublishe

Doctor of Philosophy

dissertationIn order to ensure high production yield of semiconductor devices, it is desirable to characterize intermediate progress towards the final product by using metrology tools to acquire relevant measurements after each sequential processing step. The metrology data are commonly used in feedback and feed-forward loops of Run-to-Run (R2R) controllers to improve process capability and optimize recipes from lot-to-lot or batch-to-batch. In this dissertation, we focus on two related issues. First, we propose a novel non-threaded R2R controller that utilizes all available metrology measurements, even when the data were acquired during prior runs that differed in their contexts from the current fabrication thread. The developed controller is the first known implementation of a non-threaded R2R control strategy that was successfully deployed in the high-volume production semiconductor fab. Its introduction improved the process capability by 8% compared with the traditional threaded R2R control and significantly reduced out of control (OOC) events at one of the most critical steps in NAND memory manufacturing. The second contribution demonstrates the value of developing virtual metrology (VM) estimators using the insight gained from multiphysics models. Unlike the traditional statistical regression techniques, which lead to linear models that depend on a linear combination of the available measurements, we develop VM models, the structure of which and the functional interdependence between their input and output variables are determined from the insight provided by the multiphysics describing the operation of the processing step for which the VM system is being developed. We demonstrate this approach for three different processes, and describe the superior performance of the developed VM systems after their first-of-a-kind deployment in a high-volume semiconductor manufacturing environment

Analyzing sampling methodologies in semiconductor manufacturing

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. 81-83).This thesis describes work completed during an internship assignment at Intel Corporation's process development and wafer fabrication manufacturing facility in Santa Clara, California. At the highest level, this work relates to the importance of adequately creating and maintaining data within IT solutions in order to receive the full business benefit expected through the use of these systems. More specifically, the project uses, as a case example, the sampling methodology used in the fab for metrology data collection to show that significant issues exist relating to the software Various recommendations were undertaken to improve the application's effectiveness. As part of this effort, plans for an online reporting tool were developed allowing much greater visibility into the system's ongoing performance. Initial data updates and other improvements resulted in a reduction in both product cycle times and required labor hours for metrology operations. application database and business processes concerning data accuracy and completeness. The organizational challenges contributing to this problem will also be discussed. Without a rigorous focus on the accuracy and completeness of data within manufacturing execution systems, the results of continuous improvement activities will be less than expected. Furthermore, sharing information relating to these projects across geographical boundaries and business units is vital to the success of manufacturing organizations.by Richard M. Anthony.S.M.M.B.A

Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

A constraint-based systems approach to line yield improvement in semiconductor wafer fabrication

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (p. 105-106).by Viju S. Menon.M.S