Pub Date : 1995-11-13DOI: 10.1109/ASMC.1995.484404
L. Fuller, K. Hirschman
The Integrated circuit Factory at RIT has realized much success in improving manufacturing performance and advancement toward six-sigma process capability goals. The Factory is comprised of approximately 20 graduate and undergraduate students in microelectronic engineering. Customers include a small number of companies and other universities in addition to faculty members and graduate students in electrical, microelectronic, and computer engineering at RIT. Products mostly consist of analog and digital CMOS integrated circuits fabricated in a P-well CMOS process. The factory maintains a work-in-progress (WIP) level of around 5 lots (50 wafers) and has a throughput of approximately 50 lots per year, with an average lot cycle time of approximately 1 month. The university IC facility is very dynamic in that the operators, equipment, and processes are constantly changing, The process capability baseline has been obtained from data collected for the past several years of student-run factory operation. A methodology to improve the quality of the student-run factory was implemented and is described in detail, The baseline study found that none of processes had process capability (Cpk) greater than one (3 sigma). However, manufacturing performance and product quality has been greatly improved by implementing the following set of tools: computer integrated manufacturing (CIM); total quality management (TQM) methodology; statistical process control (SPC); and "six-sigma" process capability analysis. Today several processes show Cpk>1. The student run integrated circuit factory at RIT has made significant progress toward achieving six-sigma manufacturing goals.
{"title":"Improving manufacturing performance at the Rochester Institute of Technology integrated circuit factory","authors":"L. Fuller, K. Hirschman","doi":"10.1109/ASMC.1995.484404","DOIUrl":"https://doi.org/10.1109/ASMC.1995.484404","url":null,"abstract":"The Integrated circuit Factory at RIT has realized much success in improving manufacturing performance and advancement toward six-sigma process capability goals. The Factory is comprised of approximately 20 graduate and undergraduate students in microelectronic engineering. Customers include a small number of companies and other universities in addition to faculty members and graduate students in electrical, microelectronic, and computer engineering at RIT. Products mostly consist of analog and digital CMOS integrated circuits fabricated in a P-well CMOS process. The factory maintains a work-in-progress (WIP) level of around 5 lots (50 wafers) and has a throughput of approximately 50 lots per year, with an average lot cycle time of approximately 1 month. The university IC facility is very dynamic in that the operators, equipment, and processes are constantly changing, The process capability baseline has been obtained from data collected for the past several years of student-run factory operation. A methodology to improve the quality of the student-run factory was implemented and is described in detail, The baseline study found that none of processes had process capability (Cpk) greater than one (3 sigma). However, manufacturing performance and product quality has been greatly improved by implementing the following set of tools: computer integrated manufacturing (CIM); total quality management (TQM) methodology; statistical process control (SPC); and \"six-sigma\" process capability analysis. Today several processes show Cpk>1. The student run integrated circuit factory at RIT has made significant progress toward achieving six-sigma manufacturing goals.","PeriodicalId":237741,"journal":{"name":"Proceedings of SEMI Advanced Semiconductor Manufacturing Conference and Workshop","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128541806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-11-13DOI: 10.1109/ASMC.1995.484336
N. Patel, T. Boswell, T. Nelson
As a part of a SEMATECH project to improve the cost performance of semiconductor facility fluid systems, cost models have been developed in Microsoft Excel for gas chemicals, and ultra pure water (UPW) systems. These cost models are designed to permit cost analysis associated with the acquisition, use, and maintenance of fluid systems as well as cost comparisons of various fluid supply and distribution methods using a consistent costing methodology. The system attributes to be input in the model include the flowrate, and maintenance/reliability data. The subsystem attributes to be input in the model include the capital costs, leased equipment costs, utility usage, labor requirements, and other operating costs. Based on these inputs, the total capital and operating costs are calculated for a fluid system and ten year cost analysis is performed. The output is reported in terms of cost per unit volume.
{"title":"Facility fluids cost models","authors":"N. Patel, T. Boswell, T. Nelson","doi":"10.1109/ASMC.1995.484336","DOIUrl":"https://doi.org/10.1109/ASMC.1995.484336","url":null,"abstract":"As a part of a SEMATECH project to improve the cost performance of semiconductor facility fluid systems, cost models have been developed in Microsoft Excel for gas chemicals, and ultra pure water (UPW) systems. These cost models are designed to permit cost analysis associated with the acquisition, use, and maintenance of fluid systems as well as cost comparisons of various fluid supply and distribution methods using a consistent costing methodology. The system attributes to be input in the model include the flowrate, and maintenance/reliability data. The subsystem attributes to be input in the model include the capital costs, leased equipment costs, utility usage, labor requirements, and other operating costs. Based on these inputs, the total capital and operating costs are calculated for a fluid system and ten year cost analysis is performed. The output is reported in terms of cost per unit volume.","PeriodicalId":237741,"journal":{"name":"Proceedings of SEMI Advanced Semiconductor Manufacturing Conference and Workshop","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121818526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-09-19DOI: 10.1109/ASMC.1995.484362
Raul Nañez, A. Iturralde
Current trends in semiconductor manufacturing place a large emphasis on monitoring and/or controlling costs. One of the tools used in this effort is cost of ownership modeling. Cost of ownership provides a method to monitor and control costs, evaluate projects, and gain a better understanding into the manufacturing process. From the previous literature on the subject, models can range from very simple to very complex. The need for complexity in this type of model must be evaluated with respect to the actual level of accuracy required. Quality of the data obtained is very important as inaccurate information can lead to potential misuse. From past experience, data collection for modeling can range from being easily accessible to very obscure. In the search for data, various departments such as finance, engineering, facilities, production, and many others must be consulted. The value of the information obtained versus the cost involved in obtaining this information must also be evaluated. In this paper, the development of a cost of ownership model is outlined with the emphasis being placed on the desired goals, the methods used, the sources and manipulation of the data, and a practical example. Future applications of cost of ownership modeling are also discussed as well as integration into a semiconductor production facility.
{"title":"Development of cost of ownership modeling at a semiconductor production facility","authors":"Raul Nañez, A. Iturralde","doi":"10.1109/ASMC.1995.484362","DOIUrl":"https://doi.org/10.1109/ASMC.1995.484362","url":null,"abstract":"Current trends in semiconductor manufacturing place a large emphasis on monitoring and/or controlling costs. One of the tools used in this effort is cost of ownership modeling. Cost of ownership provides a method to monitor and control costs, evaluate projects, and gain a better understanding into the manufacturing process. From the previous literature on the subject, models can range from very simple to very complex. The need for complexity in this type of model must be evaluated with respect to the actual level of accuracy required. Quality of the data obtained is very important as inaccurate information can lead to potential misuse. From past experience, data collection for modeling can range from being easily accessible to very obscure. In the search for data, various departments such as finance, engineering, facilities, production, and many others must be consulted. The value of the information obtained versus the cost involved in obtaining this information must also be evaluated. In this paper, the development of a cost of ownership model is outlined with the emphasis being placed on the desired goals, the methods used, the sources and manipulation of the data, and a practical example. Future applications of cost of ownership modeling are also discussed as well as integration into a semiconductor production facility.","PeriodicalId":237741,"journal":{"name":"Proceedings of SEMI Advanced Semiconductor Manufacturing Conference and Workshop","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130567320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ASMC.1995.484406
R. Sacco, R. Cappel
Summary form only given. This paper details the path taken to develop an optimal inspection strategy for monitoring defect levels from a CMP process. The relative unpredictability of this process can cause thickness variations across a wafer. These variations make many conventional inspection techniques unreliable. The objective of this study is to analyze the validity of using: current inspection techniques, such as laser scattering and image processing tools; new inspection techniques, such as Perspective Darkfield imaging, circular polarization, low oblique laser scattering and new imaging techniques using high Numerical Aperture objectives with various magnification changers; modifications of current techniques. The results of these tests will be compiled and analyzed to determine if current inspection techniques can be used effectively within the process flow or if new inspection techniques must be incorporated.
{"title":"Development of an optimal inspection strategy for chemical mechanical polished (CMP) wafers","authors":"R. Sacco, R. Cappel","doi":"10.1109/ASMC.1995.484406","DOIUrl":"https://doi.org/10.1109/ASMC.1995.484406","url":null,"abstract":"Summary form only given. This paper details the path taken to develop an optimal inspection strategy for monitoring defect levels from a CMP process. The relative unpredictability of this process can cause thickness variations across a wafer. These variations make many conventional inspection techniques unreliable. The objective of this study is to analyze the validity of using: current inspection techniques, such as laser scattering and image processing tools; new inspection techniques, such as Perspective Darkfield imaging, circular polarization, low oblique laser scattering and new imaging techniques using high Numerical Aperture objectives with various magnification changers; modifications of current techniques. The results of these tests will be compiled and analyzed to determine if current inspection techniques can be used effectively within the process flow or if new inspection techniques must be incorporated.","PeriodicalId":237741,"journal":{"name":"Proceedings of SEMI Advanced Semiconductor Manufacturing Conference and Workshop","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129409815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：