Surface roughness modelling with neural networks

Accurate surface modelling has become important in the modem integrated circuits manufacturing technology. On all the real surfaces microscopic roughness appears, which affects many electronic properties of the material, which in turn decides the yield and reliability of the integrated circuits. The...

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Main Authors: PATRIKAR, Rajendra M., RAMANATHAN, Kiruthika, ZHUANG, Wenjun
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Language:English
Published: Institutional Knowledge at Singapore Management University 2002
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Online Access:https://ink.library.smu.edu.sg/sis_research/7427
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-84302022-10-13T03:42:02Z Surface roughness modelling with neural networks PATRIKAR, Rajendra M. RAMANATHAN, Kiruthika ZHUANG, Wenjun Accurate surface modelling has become important in the modem integrated circuits manufacturing technology. On all the real surfaces microscopic roughness appears, which affects many electronic properties of the material, which in turn decides the yield and reliability of the integrated circuits. The surface roughness is a complex function of the processing parameters of the fabrication processes. It is difficult to express surface roughness as a function of process parameters in the form of analytical function. It is necessary to map the input parameters to roughness for a process control since it directly affects the yield and reliability of the product. In this paper we show that neural networks can be used to map these parameters to surface roughness. This approach is also suitable for model based control systems in manufacturing. 2002-11-22T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/7427 info:doi/10.1109/ICONIP.2002.1199003 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems OS and Networks
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
OS and Networks
spellingShingle Databases and Information Systems
OS and Networks
PATRIKAR, Rajendra M.
RAMANATHAN, Kiruthika
ZHUANG, Wenjun
Surface roughness modelling with neural networks
description Accurate surface modelling has become important in the modem integrated circuits manufacturing technology. On all the real surfaces microscopic roughness appears, which affects many electronic properties of the material, which in turn decides the yield and reliability of the integrated circuits. The surface roughness is a complex function of the processing parameters of the fabrication processes. It is difficult to express surface roughness as a function of process parameters in the form of analytical function. It is necessary to map the input parameters to roughness for a process control since it directly affects the yield and reliability of the product. In this paper we show that neural networks can be used to map these parameters to surface roughness. This approach is also suitable for model based control systems in manufacturing.
format text
author PATRIKAR, Rajendra M.
RAMANATHAN, Kiruthika
ZHUANG, Wenjun
author_facet PATRIKAR, Rajendra M.
RAMANATHAN, Kiruthika
ZHUANG, Wenjun
author_sort PATRIKAR, Rajendra M.
title Surface roughness modelling with neural networks
title_short Surface roughness modelling with neural networks
title_full Surface roughness modelling with neural networks
title_fullStr Surface roughness modelling with neural networks
title_full_unstemmed Surface roughness modelling with neural networks
title_sort surface roughness modelling with neural networks
publisher Institutional Knowledge at Singapore Management University
publishDate 2002
url https://ink.library.smu.edu.sg/sis_research/7427
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