FMEA-TSTM-NNGA: A Novel Optimization Framework Integrating Failure Mode and Effect Analysis, the Taguchi Method, a Neural Network, and a Genetic Algorithm for Improving the Resistance in Dynamic Random Access Memory Components

Abstract

Dynamic random access memory (DRAM) serves as a critical component in medical equipment. Given the exacting standards demanded by medical equipment products, manufacturers face pressure to improve their product quality. The electrical characteristics of these products are based on the resistance value of the DRAM components. Hence, the purpose of this study is to optimize the resistance value of DRAM components in medical equipment. We proposed a novel FMEA-TSTM-NNGA framework that integrates failure mode and effect analysis (FMEA), the two-stage Taguchi method (TSTM), neural networks (NN), and genetic algorithms (GA) to optimize the manufacturing process. Moreover, the proposed FMEA-TSTM-NNGA framework achieved a substantial reduction in experimental trials, cutting the required number by a factor of 85.3 when compared to the grid search method. Our framework successfully identified optimal manufacturing condition settings for the resistance values of DRAM components: Depo time = 27 s, Depo O2 flow = 151 sccm, ARC-LTO etch time = 43 s, ARC-LTO etch pressure = 97 mTorr, Ox-SiCO etch time = 91 s, Ox-SiCO gas ratio = 22%, and Polish time = 84 s. The results helped the case company improve the resistance value of DRAM components from 191.1 × 10−3 Ohm to 176.84 × 10−3 Ohm, which is closer to the target value of 176.5 × 10−3 Ohm. The proposed FMEA-TSTM-NNGA framework is designed to operate efficiently on resource-constrained, facilitating real-time adjustments to production attributes. This capability enables DRAM manufacturers to swiftly optimize product quality.

Keywords

• DRAM
• FMEA
• Taguchi method
• neural network
• genetic algorithm