A 3A Low Voltage Laser Diode Driver IC in a CMOS technology for an iToF-based 3D image sensor

David, Romain; Allard, Bruno; Branca, Xavier; Joubert, Charles

doi:10.23919/epe20ecceeurope43536.2020.9215735

Cited by 3 publications

(1 citation statement)

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“…This superiority arises from their remarkable mechanical stability, high-performance characteristics, and cost-effectiveness. In contrast to indirect time-of-flight (iToF) systems [ 1 , 2 ], which suffer from limited measurement distances (<20 m), substantial power consumption (tenfold that of direct time-of-flight, dToF), and convoluted calibration algorithms, the dToF imaging system offers a more promising solution. By combining the single-photon sensitivity of SPADs with the picosecond-level temporal resolution of TDCs, dToF systems enable long-range object measurement and centimeter-level depth resolution [ 3 ].…”

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%