Design of a Miniaturized Wide-Angle Fisheye Lens Based on Deep Learning and Optimization Techniques

Chuen-Lin Tien; Chun-Yu Chiang; Wen-Shing Sun

doi:10.3390/mi13091409

Design of a Miniaturized Wide-Angle Fisheye Lens Based on Deep Learning and Optimization Techniques

Micromachines (Basel). 2022 Aug 27;13(9):1409. doi: 10.3390/mi13091409.

Authors

Chuen-Lin Tien^{1

2}, Chun-Yu Chiang², Wen-Shing Sun³

Affiliations

¹ Department of Electrical Engineering, Feng Chia University, Taichung 40724, Taiwan.
² Ph.D. Program of Electrical and Communications Engineering, Feng Chia University, Taichung 40724, Taiwan.
³ Department of Optics and Photonics, National Central University, Chungli 32001, Taiwan.

Abstract

This paper presents the optimization design of a miniaturized five-element wide-angle fisheye lens using a deep learning algorithm. Zemax optical design software was used to simulate and optimize the wide-angle fisheye lens. A deep learning algorithm helped to find the best combination of different lens materials. We first used six lens elements as an initial configuration to design miniaturized wide-angle fisheye lenses using the optimization process. The optical system components were gradually decreased to five lens elements. Both OKP4HT and polymethyl methacrylate (PMMA) plastic aspheric lenses were selected to replace the second spherical glass lens in the original design. We propose two types of wide-angle fisheye lens designs with four spherical lenses and one aspheric lens. The results for these designs indicated a viewing angle of 174°, a total length of less than 15 mm, a spot size of less than 6 μm, lateral color within ±1 μm, field curvature within ±0.02 mm, and F-θ distortion of ±3.5%. In addition, the MTF value was larger than 0.4 at the spatial frequency of 100 cycles/mm.

Keywords: deep learning algorithm; modulation transfer function (MTF); optimization design; wide-angle fisheye lens.

Grants and funding

This research was supported in part by the National Science and Technology of Council, under project number MOST 111-2622-E-035-003. This study was also supported by Feng Chia University (Contract No. 21H00723).