SBIR-STTR Award

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.

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.