I-Corps: LabMate: Accelerated Empirical Process Optimization

University Of Texas At Austin, Austin TX

Investigators

Abstract

Today, recipe development and optimization for micro- and nanofabrication processes are typically based on time consuming and expensive experimental trial and error. Some processes may take up to a year to be created and fully optimized severely limiting technology development. Statistical software tools like JMP attempt to capitalize on classical design of experiment (DoE) techniques to mitigate this high cost of recipe creation and optimization, but they neglect information that might be gained from an understanding of process physics and often require large numbers of experiments for precise fits. Other process tools, including Synopsys and Coventor, lack many predictive capabilities. Shortening the development cycle offers a clear opportunity to save time and money and enables new nanoscale technologies. This I-Corps team has invented a methodology for using physics based models and integrated Bayesian statistics to dramatically speed up and reduce the cost for the empirical optimization of micro- and nanofabrication processes compared to classical DoE. The proposed technology "LabMate" employs an iterative feedback between a model constructed on a robust theoretical foundation and experiments. In addition, LabMate enables the knowledge and experience of the user to be incorporated quantitatively into the model to further decrease the time to optimization and making it well-suited for commercial application. Preliminary application of the proposed method to the development of dry etching recipes shows that the number of experiments can be reduced by a factor of two to three compared to the number of experiments required by DoE. This translates to hundreds of thousands of dollars in annual savings for etch recipe creation and optimization. Importantly, although the initial target market lies within the semiconductor space, the proposed technology can easily translate to any physical process with a large number of unknown parameters and limited experimental data. This team is targeting recipe optimization for dry etching processes at companies including LAM, Intel, Tokyo Electron, Global Foundries, Taiwan Semiconductor Manufacturing Company, and Applied Materials. An initial market survey taught the team that the few simulation tools that exist today for etch recipe creation and optimization lack the flexibility and capacity of our invention. The team will employ a subscription based model with customer support to commercialize the proposed software.