Abstract:
In recent years, the complexity of semiconductor manufacturing processes has increased, leading to a growing need for the high-precision optimization of device structures...Show MoreMetadata
Abstract:
In recent years, the complexity of semiconductor manufacturing processes has increased, leading to a growing need for the high-precision optimization of device structures. For example, in batch-type wet etching devices, the flow of chemical liquids within the process bath can vary depending on the device structure, causing variations in the etching state of the wafer. This issue is addressed using a feedback mechanism that iteratively adjusts the device structure 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 memories, word-line formation 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. This fluctuation may cause various problems. This study introduces an innovative method utilizing multi-objective Bayesian optimization to derive optimal wet etching bath design parameters; this method is informed by image and physical-quantity data from fluid dynamics simulations [1]. This approach is validated through simulation experiments, whose results are used to identify the best possible wet etching bath designs effectively.
Date of Conference: 09-10 December 2024
Date Added to IEEE Xplore: 14 February 2025
ISBN Information: