Laser Beam Homogenization Method Based on Random Microlens Array

A-xiu, 曹阿秀 Cao; Lifang, 史立芳 Shi; Jinqing, 余金清 Yu; Hui, 庞辉 Pang; Man, 张满 Zhang; Qi-ling, 邓启凌 Deng

doi:10.3788/al20153501.0124

Cited by 5 publications

(1 citation statement)

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“…Each free-form surface within the MLA introduced suitable aberrations in the wavefront, redistributing the beam’s irradiance. In the same year, Cao et al [ 18 ] presented a laser beam homogenization method that employed a central off-axis random MLA. By adjusting the off-axis quantity of the center, the MLA’s periodicity was disrupted, eliminating the periodic lattice effect on the target surface.…”

Section: Introductionmentioning

confidence: 99%

A 256 × 256 LiDAR Imaging System Based on a 200 mW SPAD-Based SoC with Microlens Array and Lightweight RGB-Guided Depth Completion Neural Network

Wang

et al. 2023

Sensors

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Light detection and ranging (LiDAR) technology, a cutting-edge advancement in mobile applications, presents a myriad of compelling use cases, including enhancing low-light photography, capturing and sharing 3D images of fascinating objects, and elevating the overall augmented reality (AR) experience. However, its widespread adoption has been hindered by the prohibitive costs and substantial power consumption associated with its implementation in mobile devices. To surmount these obstacles, this paper proposes a low-power, low-cost, single-photon avalanche detector (SPAD)-based system-on-chip (SoC) which packages the microlens arrays (MLAs) and a lightweight RGB-guided sparse depth imaging completion neural network for 3D LiDAR imaging. The proposed SoC integrates an 8 × 8 SPAD macropixel array with time-to-digital converters (TDCs) and a charge pump, fabricated using a 180 nm bipolar-CMOS-DMOS (BCD) process. Initially, the primary function of this SoC was limited to serving as a ranging sensor. A random MLA-based homogenizing diffuser efficiently transforms Gaussian beams into flat-topped beams with a 45° field of view (FOV), enabling flash projection at the transmitter. To further enhance resolution and broaden application possibilities, a lightweight neural network employing RGB-guided sparse depth complementation is proposed, enabling a substantial expansion of image resolution from 8 × 8 to quarter video graphics array level (QVGA; 256 × 256). Experimental results demonstrate the effectiveness and stability of the hardware encompassing the SoC and optical system, as well as the lightweight features and accuracy of the algorithmic neural network. The state-of-the-art SoC-neural network solution offers a promising and inspiring foundation for developing consumer-level 3D imaging applications on mobile devices.

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Section: Introductionmentioning

confidence: 99%

A 256 × 256 LiDAR Imaging System Based on a 200 mW SPAD-Based SoC with Microlens Array and Lightweight RGB-Guided Depth Completion Neural Network

Wang

et al. 2023

Sensors

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Design of random microlens arrays with large scattering angles

Song,

Huang,

et al. 2025

Optics & Laser Technology

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Fabrication of Random Microlens Array for Laser Beam Homogenization with High Efficiency

et al. 2020

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The miniaturized and integrated microlens array (MLA) can effectively achieve the beam homogenization, compactness and miniaturization of laser systems. When the high-coherence laser beam is homogenized by means of using the MLA, interference fringes will occur in the homogenized light spot due to the periodicity of the MLA, which seriously affects the uniformity of the homogenized light spot. To solve this problem, a novel random microlens array (rMLA) structure was proposed for the purpose of achieving beam homogenization. The coherence in the homogenization process is suppressed by means of breaking the periodicity of the MLA. The homogenized light spot with a high energy utilization is then obtained accordingly. In the fabrication process, a clever method of combining chemical etching with lithography technology is performed to fabricate a honeycomb rMLA and a rectangular rMLA. The experimental results show that the energy utilization rate of the two types of the rMLAs is about 90%, and the uniformity of the homogenized light spots generated by the honeycomb rMLA and the rectangular rMLA are more than 80% and 85%, respectively. Meanwhile, fully cost-effective fabrication is possible to be realized.

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Laser Beam Homogenization Method Based on Random Microlens Array

Cited by 5 publications

References 0 publications

A 256 × 256 LiDAR Imaging System Based on a 200 mW SPAD-Based SoC with Microlens Array and Lightweight RGB-Guided Depth Completion Neural Network

A 256 × 256 LiDAR Imaging System Based on a 200 mW SPAD-Based SoC with Microlens Array and Lightweight RGB-Guided Depth Completion Neural Network

Design of random microlens arrays with large scattering angles

Fabrication of Random Microlens Array for Laser Beam Homogenization with High Efficiency

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