SBIR-STTR Award

This Small Business Innovation Research (SBIR) Phase I project will address the technical barrier of manufacturing error identification which fundamentally limits the achievable yields in the production of miniature cameras. The methods employed in miniature camera manufacturing severely limit access to metrology data at the component-level. Adequate characterization data is only obtained in out-going quality control after the modules are assembled, showing manufacturers the final product performance, but giving little insight into the key contributors limiting performance. Current failure mode analysis involves destructive testing on an audit basis to attempt to identify errors. In this project an algorithm will be developed and tested which identifies key assembly and fabrication errors based on the typical outgoing quality control data. The project will develop system models to assess the minimum set of test data inputs or modifications necessary to seed a reliable algorithm free from ambiguous prediction, identify - in hardware - the test conditions necessary for valid data analysis, and determine the accuracy achievable by such an algorithm. It is anticipated that a design-aware algorithm can accurately identify manufacturing errors with real world testing conditions and minimal changes to the current out-going quality control measurement. The broader impact/commercial potential of this project is the development of a U.S. manufacturing infrastructure that relies on automation and precision engineering instead of manual labor, enabling U.S. companies to gain traction in the miniature camera market. Furthermore, the statistical manufacturing data supplied by the algorithm enables predicted performance of new designs, allowing more aggressive exploration of innovative camera solutions. The miniature camera market has seen explosive growth in the last decade as now over 70% of cell phones have cameras and more than one billion cameras are sold each year. In this high volume industry, improvements in yield and manufacturing time can have a significant impact on cost savings. The pursuit of additional cost reduction has given rise to mass manufacturing where thousands of lens elements are simultaneously fabricated and affixed to sensors, eliminating the need for costly optical barrel assembly. Due to the immaturity of the numerous processes involved in mass manufacturing of miniature cameras, fabrication errors make high yields unattainable, negating any cost savings. The commercial potential of this project is large and will enable the rapid adoption and viability of mass manufacturing as well as improving the yields of all miniature camera modules

This Small Business Innovation Research (SBIR) Phase II project will develop and test real-time process monitoring systems to support manufacturing of miniature digital cameras. Rapid growth in unit volume of digital cameras for cellphones and consumer goods has outpaced the industry?s manufacturing process monitoring technology. Except for simple pass/fail outgoing quality tests, high volume camera manufacturers lack any system for in-line, real-time monitoring of production errors that cause low yields, high production costs, and delay new product introduction. The Real-time Camera Analysis and Process Tracking algorithm, ReCAPT, integrates with existing production equipment to identify manufacturing errors and trends before product quality is compromised. ReCAPT leverages outgoing QC data, along with novel design-aware algorithms to identify assembly and fabrication errors and improve the manufacturing process. The Phase II objectives include optimizing the data collection hardware and pre-processing software, automating and generalizing the algorithm initialization, and integrating ReCAPT into the production environment through improvements to the algorithm?s robustness. With a key commercialization partner, ReCAPT will be tested multiple times in actual production environments with potential customers reviewing the results. The results will determine the achievable improvement in production efficiency, and quantify ReCAPT?s economic value to manufacturers of digital cameras. The broader impact/commercial potential of this project involves improving yields in the production of miniature camera lenses. Over one billion miniature digital cameras produced annually supply the explosive growth in cell phones and other mobile consumer electronics. The pursuit of cost reduction has led to development of wafer-level manufacturing where thousands of camera lenses are simultaneously fabricated, affixed to a wafer of image sensors and then diced ? potentially eliminating the need for individual component assembly. By improving yields and lowering costs, ReCAPT will enable the rapid adoption of wafer-level and other automated, capital intensive camera manufacturing technologies. The broader impact is the development of manufacturing technologies that rely on automation and precision engineering instead of manual labor, enabling US companies to gain traction in the growing $15 Billion annual digital camera market. The statistical manufacturing process data supplied by ReCAPT enables real-time control of manufacturing, reduces new product risk, and allows more aggressive development of innovative camera technology. Sold as an enhancement to existing automated manufacturing equipment, the ReCAPT software product will increase profit for component manufacturers, improve product performance and performance consistency for consumer goods manufacturers