Solid-State dToF LiDAR System Using an Eight-Channel Addressable, 20W/Ch Transmitter, and a 128x128 SPAD Receiver with SNR-Based Pixel Binning and Resolution Upscaling

Zhuo, Shenglong; Zhao, Lei; Xia, Tao; Wang, Lei; Shi, Shi; Wu, Yifan; Liu, Chang; Wang, Chill; Wang, Yuwei; Li, Yuan; Yu, Hengwei; Xu, Jiqing; Wang, Aaron; Lin, Zhihong; Chen, Yun; Bai, Rui; Chen, Xuefeng; Chiang, Patrick

doi:10.1109/cicc53496.2022.9772823

Cited by 6 publications

(3 citation statements)

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“…The SR-Net proposed in this work recovered more detailed depth features of the detected object. In Figure [ 16 ], the same backbone as SR-Net was adopted, but the ML-based spatial resolution up-scaling model proposed in [ 16 ] failed to improve the SNR, and spot noise was clear. In [ 10 ], the result kept the intrinsic distortion characteristics of SPAD sensors, including the line offsets, which can be corrected using a convolutional neural network.…”

Section: Resultsmentioning

confidence: 99%

“…Figure 5 shows the hardware platform including the SPAD array and VCSEL [ 16 ]. The SPAD array was fabricated with TSME 180 nm technology.…”

Section: Chip Description and Data Acquisitionmentioning

confidence: 99%

“… Performance comparison of this work with state-of-the-art dToF LiDAR systems [ 16 ] and classical dToF estimation method [ 10 ]. …”

Section: Figurementioning

confidence: 99%

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Multi-Scale Histogram-Based Probabilistic Deep Neural Network for Super-Resolution 3D LiDAR Imaging

Sun

Zhuo

Chiang

2022

Sensors

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LiDAR (Light Detection and Ranging) imaging based on SPAD (Single-Photon Avalanche Diode) technology suffers from severe area penalty for large on-chip histogram peak detection circuits required by the high precision of measured depth values. In this work, a probabilistic estimation-based super-resolution neural network for SPAD imaging that firstly uses temporal multi-scale histograms as inputs is proposed. To reduce the area and cost of on-chip histogram computation, only part of the histogram hardware for calculating the reflected photons is implemented on a chip. On account of the distribution rule of returned photons, a probabilistic encoder as a part of the network is first proposed to solve the depth estimation problem of SPADs. By jointly using this neural network with a super-resolution network, 16× up-sampling depth estimation is realized using 32 × 32 multi-scale histogram outputs. Finally, the effectiveness of this neural network was verified in the laboratory with a 32 × 32 SPAD sensor system.

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Section: Resultsmentioning

confidence: 99%

“…Figure 5 shows the hardware platform including the SPAD array and VCSEL [ 16 ]. The SPAD array was fabricated with TSME 180 nm technology.…”

Section: Chip Description and Data Acquisitionmentioning

confidence: 99%