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Applying Taguchi’s Method, Artificial Neural Network and Genetic Algorithm to Reduce the CoSi₂ Resistance Deviation of DRAM Products
Demand for products of dynamic random-access memory (DRAM) has dramatically increased since 2019. To satisfy the soaring demand, many companies have increased their supply for DRAM products. The DRAM CoSi 2 resistance significantly affects the quality of a DRAM product. The case company under this study suffered from deviations of the CoSi 2 resistances of its DRAM products from a target value of 11 ohms. The deviation of the CoSi 2 resistance has resulted in a low yield rate in manufacturing the DRAM products. In this paper, we propose a new method consisting of Taguchi's method, a neural network (NN) and a genetic algorithm (GA) to reduce the deviation of the average CoSi 2 resistance from a target value. The experimental result showed that the proposed method helped the case company to successfully reduce the deviation of its average CoSi 2 resistance from the target value of 11 ohms, from 1.440 ohms to 0.302 ohms. As a result, the yield rate has been significantly improved, and no defective DRAM products have been returned from its customers after applying the proposed method.
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