Recent Developments in AI Technology and Its Application to Wet Process Optimization

Recent advancements in artificial intelligence (AI) have increased the need for high-performance, high-capacity semiconductors. Consequently, cutting-edge semiconductor structures have become increasingly complex, requiring highly precise optimization in manufacturing processes, and achieving high yield rates within short periods has become challenging. This study focuses on the use of various yield improvement and process development methods using AI in semiconductor manufacturing and development to address these challenges. Specifically, we report that Computational Fluid Dynamics (CFD) and Multi-Objective Bayesian Optimization (MOBO) can be used in the phosphoric acid etching during the word line formation of 3D NAND Flash, to determine optimal etching conditions and hard parameters for a batch-type wet etching bath without experimental verification using wafers.