Combining neural networks and genetic algorithms for optimising the parameter design of the inter-metal dielectric process

Abstract

An inter-metal dielectric (IMD) is deposited between metal layers to provide isolation capability to a device and separate the different metal layers that are unnecessary in the conduction of electricity. Owing to the complicated input/response relationships of the IMD process, the void problem results in electric leakage and causes wafer scraping. In the current study, we combined neural networks, genetic algorithms (GAs) and the desirability function in order to optimise the parameter settings of the IMD process. Initially, a backpropagation (BP) neural network was developed to map the complex non-linear relationship between the process parameters and the corresponding responses. Moreover, the desirability function and GAs were employed to obtain the optimum operating parameters in respect to each response. The implementation of the proposed approach was carried out in a semiconductor manufacturing company in Taiwan, and the results illustrate the practicability of the proposed approach.

Keywords:

• inter-metal dielectric (IMD)
• neural networks
• genetic algorithms
• desirability function

Acknowledgements

The authors would like to thank Dr. Chao-Ton Su, chair professor of the National Tsing Hua University (Taiwan), for his full assistance. We also wish to thank the reviewers of IJPR for their positive comments on this study.