J. Semicond. > 2024, Volume 45 > Issue 10 > 101201

REVIEWS

A review of ToF-based LiDAR

Jie Ma1, Shenglong Zhuo1, , Lei Qiu1, Yuzhu Gao1, Yifan Wu1, Ming Zhong2, Rui Bai4, Miao Sun3 and Patrick Yin Chiang2

+ Author Affiliations

 Corresponding author: Shenglong Zhuo, slzhuo@tongji.edu.cn

DOI: 10.1088/1674-4926/24040015CSTR: 32376.14.1674-4926.24040015

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Abstract: In recent years, propelled by the rapid iterative advancements in digital imaging technology and the semiconductor industry, encompassing microelectronic design, manufacturing, packaging, and testing, time-of-flight (ToF)-based imaging systems for acquiring depth information have garnered considerable attention from both academia and industry. This technology has emerged as a focal point of research within the realm of 3D imaging. Owing to its relatively straightforward principles and exceptional performance, ToF technology finds extensive applications across various domains including human−computer interaction, autonomous driving, industrial inspection, medical and healthcare, augmented reality, smart homes, and 3D reconstruction, among others. Notably, the increasing maturity of ToF-based LiDAR systems is evident in current developments. This paper comprehensively reviews the fundamental principles of ToF technology and LiDAR systems, alongside recent research advancements. It elucidates the innovative aspects and technical challenges encountered in both transmitter (TX) and receiver (RX), providing detailed discussions on corresponding solutions. Furthermore, the paper explores prospective avenues for future research, offering valuable insights for subsequent investigations.

Key words: time of flightlight detection and rangingtransmitterreceiver



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Fig. 1.  (Color online) The basic working principle of the dToF and iToF. (a) The transmitter emits the modulated sine wave, and the receiver receives the reflected wave. The phase shift is obtained by the phase detector, and the distance information is obtained. (b) The transmitter emits a high-frequency square wave and generates a starting signal, which is recorded by TDC. After the receiver receives the reflected wave, a stop signal is generated, and TDC stops timing. In a certain integration time, the histogram is generally obtained by the off-chip algorithm and the peak is found, and the distance information is calculated.

Fig. 2.  (Color online) Zhou et al. built a mobile car with three kinds of sensors[26].

Fig. 3.  (Color online) The high angular resolution 360° LiDAR based on scanning MEMS mirror[29]. (a) The structure of 360° LiDAR system. (b) The schematic of TX and RX modules. (c) The LiDAR system schematic. (d) The photograph of the bird cushion. (e) The photograph of the earphone box. (f) The depth map of the bird cushion. (g) The depth map of the earphone box.

Fig. 4.  (Color online) Solid-state multi-beam scanning single-photon LiDAR system[33]. (a) Photograph of the target. (b) and (c) Depth map and reflection intensity map, generated by the proposed LiDAR system. (d) Reflection intensity map generated by a general flash LiDAR system using flood-illumination method. (e) System structure. IM: intensity modulator; AFG: arbitrary function generator; SL: spherical lens; BP: band-pass filter; TCSPC: time-correlated single-photon-counting; PC: personal computer. The solid black line represents the fiber connection, and the solid red line with the arrow represents the electric connection and the direction of signal transmission. (f) One-dimensional scan. (g) Two-dimensional scan.

Fig. 5.  (Color online) (a) Wen et al.[40] propose a new LiDAR point clouds semantic segmentation algorithm, hybrid CNN-LSTM, which is composed of an efficient point clouds feature processing method and a novel neural network structure. (b) Schematic setup of a 3D pulsed chaos LiDAR system. Chaos laser: a single-mode semiconductor laser subject to optical feedback; ISO: optical isolator; FBG: fiber Bragg grating; BOA: booster optical amplifier; BPF: band-pass filter; FC: fiber coupler; VA: variable optical attenuator; APD: avalanche photon diode; EDFA: Erbium-doped fiber amplifier; MEMS: micro-electro-mechanical systems; FPGA: field programmable gate array[39]. (c) The system processing flow and the submodel partition of the RGB-D eCNN model. A feature block pipeline is adopted in eCNN-beta to reduce latency of data load and store[39].

Fig. 6.  (Color online) Lateral and vertical structure of solid-state beam scanner[41]. (a) Illustration and microscope image of fully-integrated 32-channel scanner. The chip size is 7.5 mm × 3.0 mm. (b) Illustration and vertical SEM image of Ⅲ/Ⅴ on Si device (TLD and SOA). (c) Illustration and SEM image of TLD structure. (d) TLD wavelength and SMSR over tuning range. (e) OPA calibration process for heaters of TLD and phase shifters (PS). Combination of genetic algorithm (GA) and hill climb method is applied to obtain optimized beam profile.

Fig. 7.  (Color online) Concept of a metasurface-augmented FoV LiDAR demonstrated by Martins et al.[43]. (a) Schematic representation of the LiDAR system. (b) Top view photography of the optical setup. (c) Detail of the cascaded AOD-metasurface assembled deflection system. (d) SEM image of the metasurface showing the nanopillar building blocks of varying sizes employed to achieve beam deflection by considering lateral effective refractive index variations.

Fig. 8.  (Color online) Zhuo et al.[47] proposed a dToF solid-state LiDAR that integrates an 8-channel address programmable transmitter and a 128 × 128 SPAD receiver. (a) Cross section and (b) block diagram of the proposed MC TX. (c) Micrograph of the LDD ASIC die. (d) Block diagram of the SPAD sensor. (e) Die micrograph of the sensor chip. (f) Spatial resolution upscaling.

Fig. 9.  (Color online) (a) The pixel array is divided into four quadrants, with each sub-array allocating its own timing circuitry, partial-histogramming readout (PHR) blocks, and data pads[57]. (b) It shows the chip organization, eight I/O pads generate a modest 31.6 Mb/s of pixel data at the targeted 30 frames/s (a maximum of 800 Mb/s at 760 frames/s)[5]. (c) Its focal plane consists of 32 × 32 macro pixel array. Each of the SPADs has its own passive quenching circuit (PQC) block to ensure a reliable quenching process[68].

Fig. 10.  (Color online) (a) Optical layout in work[69] (M: mirror, L: lens, PD: photodiode, QWP: quarter-wave plate, MCCD: micro-polarizer CCD camera). (b) The target scene consists of objects with different polarization states and depths. Light emitted from the scene is split and focused on the photosensor, forming two images of orthogonal linear polarizations. The red and blue colors correspond to x- and y-polarized light, respectively[71]. (c) A subsection of receiver of the 3D image sensor based on FMCW[32]. (d) The SEM images of a fabricated device, where the micromirror is suspended by four ISC electrothermal Al/SiO2 bimorph actuators. The initial elevation of the mirror plate shown in (d) is about 145 μm. The actuators form dual-shaped structures because of the residual stresses in the bimorph beams. And (e) is the meshed bimorph actuator structure[70].

Fig. 11.  (Color online) A automotive direct time-of-flight LiDAR presented by Zou et al.[67]. (a) Proposed CI-TIA-based AFE, (b) hybrid pulse position and width converter (PPWC) architecture and (c) intensity-guided window-size-adapting accumulation (IGWAA) algorithm and CNN-guided in-painting. And a flash LiDAR sensor with fully analog-assisted in-pixel histogramming TDC based on SAR ADC[72]. (d) Schematic of the TPC. (e) Schematics and operating example of the TAC. (f) Block diagram of the proposed in-pixel histogramming TDC.

Fig. 12.  (Color online) Trends in some key parameters of LiDAR systems over the past decade.

Table 1.   Comparison of iToF and dToF.

CharacteristiciToFdToF
MethodMeasure the phase delay of the reflected lightDetect the photon round-trip traveling time
Distance/precisionSuitable for short-distance, high-precision rangingThe distance can be far (depending on optical power) and the accuracy is good.
Error sourceAmbiguous results exist when the phase is larger than 2π, system noise includes read noise components, such as thermal, flicker, ADC, and charge uncertainty.Background noise, oscillator error, quantization error such as bin width of TDC, integration time that effects SNR
Spatial resolutionSmall pixel size, high resolutionLarge pixel size, low resolution
System frame rateLowHigh
Power consumptionAs the distance increases, the average power consumption increases sharply.Low average power consumption
DownLoad: CSV

Table 2.   Comparison of Solid-state LiDAR and mechanical LiDAR[26].

Parameters Solid-state LiDAR Mechanical LiDAR
Point clouds/second 100 000−150 0000 300 000−350 0000
Ranging capability (m) 250−350 100−200
FOV Horizontal 15°−120°
Vertical 8°−70°
Horizontal 360°
Vertical 22.5°−105.2°
Ranging accuracy (cm) 2 2−3
Price (US$) 599−1200 2400−150 000
Weight (kg) 0.7−1.5 0.8−14
DownLoad: CSV

Table 3.   Comparison of device parameters and test results.

Metrics 2013[49] 2014[50] 2015[51] 2016[52] 2017[53] 2018[54] 2018[55] 2018[56] 2019[57] 2019[58]
Technology (nm) 180 350 180 150 350 45/65 180 350 180 40/90
Pixel array 202 × 96 32 × 32 64 × 32 64 × 64 1 × 80 256 × 256 32 × 32 80 × 25 252 × 144 256 × 256
Pixel pitch (μm) 30 150 60 100 19.8 28.5 28.5 9.2
Pixel fill factor (%) 70 3 26.5 19 31.3 28 32 28 51
Illum. wavelength (nm) 870 750 808 470 905 532 637 864 637 671
Illum. frequency (MHz) 0.133 40 200 1 40 0.1 40 1.9
Illum. power (mW) 21 90 47.7 6 2 0.1 2 1.8
Chip power (mW) 4000 93.5 1005 310 66 2540 77.6
Field of view (deg.) 55 × 9 6.9 × 6.9 40 × 20 2 × 40 5 × 5 18 × 28 40 × 20 1.2 × 1.2
Frame rate (fps) 10 6 100 7.68 10 6 9 30 30
Background light (klux) 70 0.05 100a 90 0.05 0.2 dark 1
Max. distance (m) 100 8 40 376 12 430 50 3.5 50 50
1σ error (%) 0.14 0.13 0.11 0.01 0.57
Precision 0.13% 1.5% 0.37% 0.40% 0.14% 0.88% 0.17% 0.34%
Metrics 2019[59] 2020[60] 2020[61] 2020[62] 2021[28] 2021[63] 2021[64] 2022[65] 2024[66] 2024[67]
Technology (nm) 180 28 130 350 90/40 65 45 110 110 65
Pixel array 113 × 129 240 × 192 64 × 64 32 × 128 189 × 600 240 × 160 256 × 128 64 × 64 320 × 240 256 × 192
Pixel pitch (μm) 30 60 40 10 16 7 48 14
Pixel fill factor (%) 45 66 35 49.7 12.9
Illum. wavelength (nm) 532 905 532/901 810 905 940 780 905 940 905
Illum. frequency (MHz) 300 0.25 10
Illum. power (mW) 6 50 1 90 5 5.8−9.3 30
Chip power (mW) 733 180 1192 51.9
Field of view (deg.) 8.7 × 7.4 24 × 42 63 × 41 2 × 2 25 × 25 17 × 15
Frame rate (fps) 1.5 10 8300 10 20 25 40 30
Background light (klux) outdoor 70 0.2 117 500 10 30 100
Max. distance (m) 11 225 10 150−200 9.5 8 8.2 48 240
1σ error (%) 0.11 0.10 0.38 0.18
Precision 0.64% 0.0073 m 0.88% 0.12% 0.10%
a Unit is Mph/s/pix.
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    Received: 10 April 2024 Revised: 28 May 2024 Online: Accepted Manuscript: 27 June 2024Uncorrected proof: 01 July 2024Published: 15 October 2024

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      Jie Ma, Shenglong Zhuo, Lei Qiu, Yuzhu Gao, Yifan Wu, Ming Zhong, Rui Bai, Miao Sun, Patrick Yin Chiang. A review of ToF-based LiDAR[J]. Journal of Semiconductors, 2024, 45(10): 101201. doi: 10.1088/1674-4926/24040015 ****J Ma, S L Zhuo, L Qiu, Y Z Gao, Y F Wu, M Zhong, R Bai, M Sun, and P Y Chiang, A review of ToF-based LiDAR[J]. J. Semicond., 2024, 45(10), 101201 doi: 10.1088/1674-4926/24040015
      Citation:
      Jie Ma, Shenglong Zhuo, Lei Qiu, Yuzhu Gao, Yifan Wu, Ming Zhong, Rui Bai, Miao Sun, Patrick Yin Chiang. A review of ToF-based LiDAR[J]. Journal of Semiconductors, 2024, 45(10): 101201. doi: 10.1088/1674-4926/24040015 ****
      J Ma, S L Zhuo, L Qiu, Y Z Gao, Y F Wu, M Zhong, R Bai, M Sun, and P Y Chiang, A review of ToF-based LiDAR[J]. J. Semicond., 2024, 45(10), 101201 doi: 10.1088/1674-4926/24040015

      A review of ToF-based LiDAR

      DOI: 10.1088/1674-4926/24040015
      CSTR: 32376.14.1674-4926.24040015
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      • Jie Ma obtained his B.S. degree from Xidian University in Xi’an, China, in 2017, followed by a M.S. degree from Leibniz University Hannover, Germany. Currently, he is pursuing his Ph.D. at Tongji University in Shanghai, China. From 2021 to 2023, he served as a digital design engineer at PhotonIC Technologies Inc. in Shanghai, specializing in the digital design of high-speed mixed-signal circuits, as well as circuits and sensors based on time of flight (ToF) technology. His current research focuses on high-speed and high-precision light detection and ranging (LiDAR) systems, as well as neural network accelerators
      • Shenglong Zhuo received his Ph.D. from Fudan University in 2022. He has worked in Silergy Corporation, Nanjing and PhotonIC Technology, Shanghai, engaged in power management and optoelectronic integrated circuit design and research, and is now a researcher at the School of Electronic and Information Engineering, Tongji University. In the past 5 years, he has published more than 20 papers in high-level journal conferences in the field of integrated circuits such as IEEE JSSC, TCAS-Ⅱ, CICC, ESSCIRC, ASSCC, and has dozens of international/domestic patents
      • Corresponding author: slzhuo@tongji.edu.cn
      • Received Date: 2024-04-10
      • Revised Date: 2024-05-28
      • Available Online: 2024-06-27

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