Surrogate neural network-based models have been lately trained and used in a variety of science and engineering applications where the number of evaluations of a target function is limited by execution time. In cell phone camera systems, various errors, such as interferences at the lens-barrel and lens-lens interfaces and axial, radial, and tilt misalignments, accumulate and alter profile of the lenses in a stochastic manner which ultimately changes optical focusing properties. Nonlinear finite element analysis of the stochastic mechanical behavior of lenses due to the interference fits is used on high-performance computing (HPC) to generate sufficient training and testing data for subsequent deep learning. Once properly trained and validated, the surrogate neural network model enabled accurate and almost instant evaluations of millions of function evaluations providing the final lens profiles. This computational model, enhanced by artificial intelligence, enabled us to efficiently perform Monte-Carlo analysis for sensitivity and uncertainty quantification of the final lens profile to various interferences. It can be further coupled with an optical analysis to perform ray tracing and analyze the focal properties of the lens module. Moreover, it can provide a valuable tool for optimizing tolerance design and intelligent components matching for many similar press-fit assembly processes.
翻译:最近对以超导神经网络为基础的模型进行了培训,并应用于各种科学和工程应用中,对目标功能的评价数量因执行时间而受到限制。在手机相机系统中,各种错误,如镜头管和镜头镜头界面的干扰,以及轴向、辐射和倾斜不匹配,以及透镜的积聚和变化剖面,以随机方式最终改变光聚焦特性。对干扰下透镜的随机机械行为进行非线性有限要素分析,用于高性能计算(HPC),以生成足够的培训和测试数据,供随后的深层学习。在经过适当培训和验证后,超导神经网络模型使得能够对提供最后透镜剖面图的数以百万计的功能评价进行准确和几乎即时的评价。这一计算模型通过人工智能得到加强,使我们能够高效地进行蒙特-卡洛分析,以敏感度和不确定地量化最终透镜剖面到各种干扰中的特性。还可以进一步结合光学分析,以进行射线追踪和分析镜头模块的核心特性。此外,它还可以提供一种宝贵的工具,用以进行类似的容忍设计。