SBIR Phase I: Yield Improvement for Miniature Cameras

这个小企业创新研究（SBIR）第一阶段项目将解决制造错误识别的技术障碍，这从根本上限制了微型相机生产的可实现产量。微型相机制造中使用的方法严重限制了对部件级计量数据的访问。充分的表征数据仅在组件组装后的外部质量控制中获得，向制造商展示最终产品性能，但对限制性能的关键因素知之甚少。当前的失效模式分析包括在审计的基础上进行破坏性测试，以试图识别错误。在这个项目中，将开发和测试一种算法，该算法基于典型的出厂质量控制数据来识别关键的装配和制造错误。该项目将开发系统模型，以评估最小的测试数据输入集或必要的修改，以播种可靠的算法，避免模棱两可的预测，在硬件上识别有效数据分析所需的测试条件，并确定这种算法可实现的准确性。预计设计感知算法可以在真实世界的测试条件下准确识别制造误差，并且对当前的出厂质量控制测量变化最小。该项目的更广泛的影响/商业潜力是美国制造基础设施的发展，依赖于自动化和精密工程而不是手工劳动，使美国公司在微型相机市场上获得牵引力。此外，该算法提供的统计制造数据可以预测新设计的性能，从而可以更积极地探索创新的相机解决方案。微型相机市场在过去十年中经历了爆炸式的增长，现在超过70%的手机都有摄像头，每年销售超过10亿个摄像头。在这个高产量的行业中，产量和制造时间的改进可以对成本节约产生重大影响。对进一步降低成本的追求导致了大规模生产，同时制造数千个透镜元件并粘贴到传感器上，从而消除了对昂贵的光学桶组件的需求。由于大规模生产微型相机的众多工艺不成熟，制造错误使高产量无法实现，从而抵消了任何成本节约。这个项目的商业潜力是巨大的，将使大规模生产的快速采用和可行性，以及提高所有微型相机模块的产量。

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.