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: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2002
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/7427 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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