A Novel Methodology for Lens Matching in Compact Lens Module Assembly

Abstract

Lens matching is a trial assembly step in compact lens module manufacturing that can effectively improve the production yield and compensate for the manufacturing error to some degree. However, conventional lens matching (i.e., through trial and error (T&E)) is extremely time-consuming and has a low success rate. To address this issue, this paper proposes a novel Genetic Algorithm (GA) based solution to improve the assembly success rate and the efficiency of lens matching. The study first formalizes the lens matching problem into an optimization format. Then, a DNN based surrogate model is built to replace expensive optical simulators in the optimization iterations. The DNN is used because it demonstrates a great balance between prediction accuracy and computation efficiency compared to other alternatives, e.g., Linear Regression (LR), Kriging, and Polynomial Chaos Expansion (PCE). Next, a GA-based solver is developed to find optimal lens matching operations. The effectiveness of the proposed method and its superiority over conventional lens matching is validated in the real-world lens production line through collaboration with the industry. Compared with conventional lens matching, the proposed method can significantly improve the production yield and lens matching efficiency. Note to Practitioners—This paper is motivated by a practical need for lens matching in the manufacturing process of the image capturing compact lens modules. The conventional practice of lens matching is tedious, time-consuming, and easy to fail. To address this issue, we mathematically characterize the lens matching problem into an optimization format that can practically be solved by the Genetic Algorithm (GA) or other methods. We then demonstrate the workflow to search for optimal lens matching solutions based on a DNN surrogate model. Simulative studies and preliminary real-world experiments indicate the proposed method can effectively improve overall production yield and is much more efficient than conventional lens matching. The results in mass production further justified the superiority of the proposed method over conventional lens matching.