  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.
