Optimal Design of Wet Etching Bath for 3-D Flash Memories Using Multi-Objective Bayesian Optimization

Recently, the complexity of semiconductor manufacturing processes has increased, resulting in a growing need for high-precision optimization of device structures. For example, in batch-type wet etching devices, the flow of chemical liquids in the process bath can vary depending on the device structure, which causes variations in the etching state of the wafer. This issue is addressed using a feedback mechanism that adjusts the device structure iteratively based on the results of an etching experiment, thereby achieving more uniform etching conditions. However, this approach requires a large number of trial experiments. In the fabrication process of 3D flash memory devices, the formation of word lines in the silicon substrate requires precise control of the silicon concentration in the etching solution. However, this concentration can fluctuate due to the dissolution of the SiN film during the etching process, which can cause various problems. Thus, this study proposes an innovative multi-objective Bayesian optimization method that is informed by image and physical quantity data from fluid dynamics simulations to derive optimal wet etching bath design parameters. The proposed method was validated through simulation experiments, and the simulation results were used to identify the best possible wet etching bath designs.