Applying PSO-based BPN for predicting the yield rate of DRAM modules produced using defective ICs

Dynamic random access memory (DRAM) modules are an important component of electronic equipment, impacting the quality, performance, and price of the final product. A typical DRAM module is composed of DRAM integrated circuits (ICs). DRAM ICs of higher quality can be used to produce DRAM modules of higher quality. However, high-quality DRAM ICs are more costly. Some DRAM module manufacturers purchase batches of DRAM ICs containing defective units and then have the batch tested in order to select DRAM ICs for production of DRAM modules. This type of DRAM module is suitable only for products not intended for work in harsh environments or being sold in lower price markets. Due to the lower quality of the DRAM ICs, the actual quality of the DRAM module is not easily predicted. Predicting the yield rate of the DRAM module is thus an important issue for DRAM module manufacturers who purchase DRAM ICs of lower quality. This study uses a back-propagation neural network (BPN) to predict the yield rate of the DRAM modules produced using defective DRAM ICs. BPN is a very capable method and has been successfully applied across many fields. However, network parameters and input features differ depending on the application. Thus, a particle swarm optimization approach is proposed to obtain suitable parameters for the BPN and to select beneficial subsets of features which result in a better prediction of the DRAM module yield rate. The experimental results showed that it outperforms traditional stepwise regression analysis.