A PROCESS PARAMETERS DETERMINATION MODEL BY INTEGRATING ARTIFICIAL NEURAL NETWORK AND ANT COLONY OPTIMIZATION

ABSTRACT

In this paper, a methodology which integrates data mining (DM) and ant colony optimization (ACO) is proposed for process parameters determination of the chemical mechanical polishing (CMP) processes in semiconductor manufacturing. In the proposed method, an Artificial Neural Network (ANN) is first studied to realize the training process between inputs and outputs of network. However, due to the invisibility in the solution procedures of ANN, the decision tree approach of Data Mining is adopted to analyze and provide the necessary information for ACO. The simulation results demonstrated the proposed method provides an efficient tool for parameters selection for the initial CMP process.

Keywords:

• Ant Colony Optimization (ACO)
• Artificial Neural Network (ANN)
• Data Mining
• Chemical Machine Polishing (CMP)