SBIR Phase I: 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 I project is that it will enable technologies on the cutting-edge like Spin Torque Transfer-Random Access Memory (STT-RAM) and high efficiency solar cells to permeate the marketplace. Currently, semiconductor applications like these technologies face significant nanomanufacturing challenges. In fact, fabricating semiconductor devices is so challenging that 55% of new semiconductor products fail to meet their original launch date and over 40% of development projects exceed the planned budgets. With its proposed virtual recipe development environment, this SBIR project will allow semiconductor chip and equipment manufacturers to save up to 66% of process development costs, gain market share through three times faster development cycles, and enable the process development of next-generation of high performance, energy efficient electronic devices. The proposed project will use statistical self-learning inference algorithms, sophisticated process models, and the vast amount of available fab data to make high accuracy process predictions and enable high volume manufacturing of innovative nanotechnologies. Three key components will be developed: (1) A recipe analytics engine capable of performing process predictions and process design for multiple process objectives, (2) A topography simulator for profile prediction of nanofeatures, and (3) A commercial platform interface for real-time process development decision making. The components will be devised to accurately capture large process systems and multiple process objectives, reduce computational expense, and facilitate commercial adoption. These innovations will be accomplished by: (a) Using parallel computing to reduce computational expense, (b) Developing reduced-order plasma and surface kinetic models, (c) Employing adaptive algorithms to accelerate recipe optimization, and (d) Using uncertainty analysis techniques for recipe co-optimization. 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.