SBIR Phase II: Disruptive Semiconductor Software Tool for Recipe Optimization for Deposition and Etching Processes

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is the enablement of next generation manufacturing technologies for semiconductor devices. The deployment of novel semiconductor devices such as 3D NAND, the latest device architecture developed for flash memory, is often stymied by the development of nanomanufacturing processes. The objective of this proposal is to build upon a recipe prediction platform to create a standalone software solution for semiconductor equipment and chip manufacturers to rapidly optimize their processes. In this research, the software speed will be increased, prediction capabilities will be extended to 3D structures, and model calibration will be automated to facilitate customer deployment. By accelerating the development of the processes used in device fabrication, this project reduces the cost of recipe development in the semiconductor industry and bringing next-generation technologies to fruition. This Small Business Innovation Research (SBIR) Phase II project will develop new advanced physical and statistical models to quickly and accurately predict outcomes for processes like plasma etch and deposition. In this research, 2D and 3D profile simulations will be algorithmically optimized for speed to give process engineers instantaneous results. Machine learning models will be used to enable process engineers to rapidly explore complex trade spaces. Sophisticated numerical algorithms will be developed to help engineers maximize process windows. Innovations in statistical and physics-based tools will permit process engineers to build and calibrate models themselves, enabling next-generation technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.