Yield Improvement for Miniature Cameras

Award Information
Agency:
National Science Foundation
Branch
n/a
Amount:
$180,000.00
Award Year:
2010
Program:
SBIR
Phase:
Phase I
Contract:
1014243
Award Id:
99144
Agency Tracking Number:
1014243
Solicitation Year:
n/a
Solicitation Topic Code:
M2
Solicitation Number:
n/a
Small Business Information
5470 Conestoga Ct, Boulder, CO, 80301
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
831559716
Principal Investigator:
Kenny Kubala
MME
(303) 900-2317
Kenny.Kubala@fivefocal.com
Business Contact:
Kenny Kubala
MME
(303) 900-2317
Kenny.Kubala@fivefocal.com
Research Institution:
n/a
Abstract
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.

* information listed above is at the time of submission.

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