Advanced process control for furnace systems in semiconductor manufacturing

Abstract

Traditional semiconductor manufacturing relies on statistical process control and human intervention as the fundamental method to change the process parameters to generate desired output. With the increase in demand for quality, yield, throughput, and performance, Advanced Process Control (APC) is needed to improve the manufacturing processes using run-to-run, wafer-to-wafer, within wafer and real-time process control. In this paper, multivariate, model-based APC system is developed using feed-forward and feedback mechanisms to automatically determine the optimal recipe parameters for each batch based on both incoming wafers and tool state properties. The APC methodology discussed in this paper mostly concentrates on diffusion batch processes. Primarily, it is implemented on the depletion processes such as Low Pressure Chemical Vapor Deposition (LPCVD) nitride. In addition to the traditional LPCVD nitride, APC models have also been tested and deployed on advanced Atomic Layer Deposition (ALD) nitride batch furnace systems. Implementation of APC includes process characterization and understanding of the reaction mechanism of each process. Effects of temperature, time and number of ALD cycles govern the fundamental equations of the model that determine the inputs for subsequent batch. The integration of Equipment Interface (EI), Mathematical Model and the furnace Tokyo Electron Ltd. (TEL) was pivotal for the success of the project. Significant improvement in run to run, lot to lot and wafer to wafer variation in thickness is demonstrated by implementation of APC. In addition to the process parameters, batch size effect is also considered for the APC roll out.