Application of Extracted Rules From a Multilayer Perceptron Network to Moulding Machine Cycle Time Improvement

Product delivery time is as vital as the yield rate for semiconductor manufacturing companies since the industry has become highly competitive and more dynamic nowadays. In the manufacturing process, appropriate parameter settings can shorten the machine cycle time and in turn the product delivery time. This paper presents the application of ´IF-THEN´ rules extracted from a multilayer perceptron (MLP) artificial neural network to tune moulding machine parameters to improve the machine cycle time. We can extract the ´IF-THEN´ rules by scrutinising the connecting weights inside the MLP network. Historical genuine operating data of a moulding machine input parameters and cycle time were collected from an integrated circuit packaging company in Taiwan. The data were fitted in with an MLP network, and the ´IF-THEN´ rules were extracted afterwards. The ´IF-THEN´ rules not only indicate which input parameters dominate the machine cycle time but also explain how to tune them. We have applied the extracted rules to tune the moulding machine parameters. Practical results show that the moulding machine cycle time agrees very well with the extracted rules, and also justify the feasibility of the proposed method.