Hamamatsu Photonics K.k. · TSE:6965

Hamamatsu Photonics K.k — Technical Presentation (December 2017)

66 slides from Hamamatsu Photonics K.k's technical presentation, Dec 6, 2017

1. Slide 1: LiDAR and Other Techniques

   LiDAR and Other Techniques Measuring Distance with Light for Automotive Industry Slawomir Piatek Technical Consultant, Hamamatsu Corp. Copyright © Hamamatsu Photonics K.K. All Rights Reserved

2. Slide 2: Introduction

   Introduction • There is a great interest in the automotive industry to develop on-vehicle systems which make driving safer. • In addition, motivated by market demand, a longer-term goal is development of a completely autonomous (self-driving) vehicle. Copyright © Hamamatsu Photonics K.K. All Rights

3. Slide 3: Introduction

   Introduction • Such a self-driving vehicle must have an ability to create a 3D map of its surroundings up to about 300 m at a video rate. This webinar discusses techniques and challenges of measuring distance with light for automotive applications emphasizing time-of-flight LiDAR Copyright © Hamamat

4. Slide 4: Outline

   Outline • Time of flight (Tof) LiDAR (emphasis of this webinar) - Basic concept - Challenges in designing ToF LiDAR - Types of ToF LiDAR: mechanical, flash, optical phase array • FMCW radar (concept) • FMCW LiDAR (heterodyne optical mixing) • Summary and conclusions Copyright © Hamamatsu Photonics K

5. Slide 5: Basic layout of ToF LiDAR

   Basic layout of ToF LiDAR “start” pulse “fast” photodetector emitted pulse laser w = cτ beam splitter emission optics to target timer returned pulse after Δt “stop” pulse collection optics Copyright © Hamamatsu Photonics K.K. All Rights Reserved 5

6. Slide 6: ToF LiDAR distance

   ToF LiDAR distance Measure Δt R = ½cΔt If Δt = 0.67 μs, R = 100 m or 6.7 ns per 1 m of distance Copyright © Hamamatsu Photonics K.K. All Rights Reserved 6

7. Slide 7: Distance uncertainty

   Distance uncertainty δR = ½cδΔt Laser spot small compared to the target feature δR – Distance uncertainty δΔt – Uncertainty in measuring Δt (mostly due to photodetector jitter) Copyright © Hamamatsu Photonics K.K. All Rights Reserved 7

8. Slide 8: Distance uncertainty

   Distance uncertainty δR = ½cτ =½w Laser spot large compared to δR – Distance uncertainty target features τ – Pulse duration w – Pulse width (cτ) w = c.τ Propagating divergent pulse Copyright © Hamamatsu Photonics K.K. All Rights Reserved 8

9. Slide 9: Beam Divergence

   Beam Divergence emitting element θ λ R St D θ Diffraction causes beam divergence: θ ≈ 1.22λ/D St – Minimum resolvable transverse size at distance R Copyright © Hamamatsu Photonics K.K. All Rights Reserved 9

10. Slide 10: Beam Divergence

    Beam Divergence Radar: 77 GHz −> λ = 0.3 cm. If D = 20 cm −> θ ≈ 1° −> St ≈ 1.8 m + 0.2 m = 2 m @ R = 100 m LiDAR: 1550 nm If D = 5 mm −> θ ≈ 0.02° −> St ≈ 3.7 cm @ R = 100 m For high-resolution 3D map, we need LiDAR Copyright © Hamamatsu Photonics K.K. All Rights Reserved 10

11. Slide 11: ToF LiDAR: timing

    ToF LiDAR: timing ST start pulse T start pulse T start pulse T – repetition period trigger level T must be > Δt time SR Δt Δt trigger level stop pulse stop pulse time Copyright © Hamamatsu Photonics K.K. All Rights Reserved 11

12. Slide 12: ToF LiDAR: maximum distance

    ToF LiDAR: maximum distance c Rmax = ½cT = ½ f f – Repetition frequency or sampling frequency Photon budget imposes another limit on Rmax Copyright © Hamamatsu Photonics K.K. All Rights Reserved 12

13. Slide 13: ToF LiDAR: minimum distance (ideal case)

    ToF LiDAR: minimum distance (ideal case) τ τ uniform pulses T>τ B=∞ ideal photodetector T Δt response trigger level tr = τ time tr tr There is no limit on the smallest distance Copyright © Hamamatsu Photonics K.K. All Rights Reserved 13

14. Slide 14: ToF LiDAR: minimum distance (realistic)

    ToF LiDAR: minimum distance (realistic) τ τ uniform pulses T>τ T B finite photodetector response non-ideal trigger level tr > τ pileup time tr Signal pileup limits the smallest measurable distance Copyright © Hamamatsu Photonics K.K. All Rights Reserved 14

15. Slide 15: ToF LiDAR: maximum sampling rate

    ToF LiDAR: maximum sampling rate fmax = 1/Δtmax = c/2Rmax fmax = 1.5 MHz @ R = 100 m Larger the range, more time it takes to produce a 3D map Copyright © Hamamatsu Photonics K.K. All Rights Reserved 15

16. Slide 16: ToF LiDAR: challenges

    ToF LiDAR: challenges Challenges and considerations in designing ToF LiDAR Copyright © Hamamatsu Photonics K.K. All Rights Reserved 16

17. Slide 17: ToF LiDAR challenges: surround view

    ToF LiDAR challenges: surround view 360 Would like R = 100 m ° 100 m range minimum 360° azimuthal coverage 20 20° declination coverage ° 0.2° resolution (~35 cm @ 100 m) Video rate, 20 frames/s Copyright © Hamamatsu Photonics K.K. All Rights Reserved 17

18. Slide 18: ToF LiDAR challenges: sampling rate

    ToF LiDAR challenges: sampling rate To meet the challenge, we need 3.6×106 samplings/s (3.6 MHz) Can do 1.5 MHz with one light source and photodetector @ R =100 m Need to compromise and/or invent different approaches Copyright © Hamamatsu Photonics K.K. All Rights Reserved 18

19. Slide 19: ToF LiDAR challenges: light source

    ToF LiDAR challenges: light source Safe for human vision Short-duration pulses (can get 2 - 5 ns) at high repetition High peak power per pulse Must comply with admissible exposure limit (AEL), which is a complex function of wavelength, repetition rate, and energy per pulse. Narrow bandwidth Copyrigh

20. Slide 20: ToF LiDAR challenges: photon budget

    ToF LiDAR challenges: photon budget illumination normal A0 specular reflection diffuse reflection target Copyright © Hamamatsu Photonics K.K. All Rights Reserved 20

21. Slide 21: ToF LiDAR challenges: photon budget

    ToF LiDAR challenges: photon budget A0 P(R) = P0ρ 2 η0exp(-2γR) πR P(R) – Power received This LiDAR equation assumes normal incidence, P0 – Peak power transmitted Lambertian reflection, flat beam profile and ρ – Target reflectivity negligible divergence, laser spot smaller than the target, and γ ind

22. Slide 22: ToF LiDAR challenges: photon budget

    ToF LiDAR challenges: photon budget Figure from Williams (2017) Number of photons (λ = 1550 nm) expected from a target as a function of its range using 1,10, 100, and 1000 nJ pulses. The figure assumes target reflectivity of 30%, 70% optical efficiency, 30-mm diameter receiver, 0.5 mrad laser beam d

23. Slide 23: ToF LiDAR challenges: photon budget

    ToF LiDAR challenges: photon budget P(R)SλM VBIAS SNR(R) = √ 2eB[(P(R)+PB)Sλ + ID]FM2 + 4kTB R0 APD Sλ – Detector’s sensitivity B – Detection bandwidth R0 M – Detector’s intrinsic gain PB – Background light optical power ID – Detector’s dark current e – elementary charge; k – Boltzmann constant; T –

24. Slide 24: ToF LiDAR challenges: what wavelength?

    ToF LiDAR challenges: what wavelength? 1550 nm + Best eye safety + Lower background - Requires IR (non-silicon) photodetectors 905 nm + Better transmission in atmosphere + Silicon-based photodetector Copyright © Hamamatsu Photonics K.K. All Rights Reserved 24

25. Slide 25: ToF LiDAR challenges: what wavelength?

    ToF LiDAR challenges: what wavelength? Spectral irradiance μW cm-2 nm-1 Solar irradiance at sea level PB @ 1550 nm < PB @ 905 nm Figure from Matson et al. 1983 905 nm 1550 nm Wavelength [nm] Copyright © Hamamatsu Photonics K.K. All Rights Reserved 25

26. Slide 26: ToF LiDAR challenges: what wavelength?

    ToF LiDAR challenges: what wavelength? Absorption coefficient of water [cm-1] From Wojtanowski et al. 2014 Wavelength [nm] H2O absorption @ 1550 nm > (100×) @ 905 nm Copyright © Hamamatsu Photonics K.K. All Rights Reserved 26

27. Slide 27: 905 nm versus 1550 nm

    905 nm versus 1550 nm Adapted from Wojtanowski et al. 2014 Reflectivity ρ [%] Wavelength [nm] Wet surface reflects more poorly @1550 nm versus @ 905 nm Copyright © Hamamatsu Photonics K.K. All Rights Reserved 27

28. Slide 28: ToF LiDAR challenges: photodetector

    ToF LiDAR challenges: photodetector + High photosensitivity + High gain + Small jitter + Small excess noise Copyright © Hamamatsu Photonics K.K. All Rights Reserved 28

29. Slide 29: Importance of jitter

    Importance of jitter t1 photodetector Detector output t2 t1, t2, t3, … transit times. Jitter represents variation in transit times. t3 t=0 time Jitter is the main contributor to δΔt which affects distance resolution. 100 ps jitter implies 1.5 cm depth uncertainty. Copyright © Hamamatsu Photonics K.K

30. Slide 30: Importance of detector gain

    Importance of detector gain signal electronic noise Output signal? Arb. noise + = time photodetector No Gain electronics Copyright © Hamamatsu Photonics K.K. All Rights Reserved 30

31. Slide 31: Importance of detector gain

    Importance of detector gain Yes signal! Output electronic noise Arb. + = time photodetector Yes Gain electronics Copyright © Hamamatsu Photonics K.K. All Rights Reserved 31

32. Slide 32: Importance of excess noise

    Importance of excess noise Signal Waveform 1 Fixed trigger level Constant fraction trigger Waveform 2 time Δt1 Δt2 Fixed trigger level gives different round-trip-times (Δt1 ≠ Δt2) ✖ Δt1= Δt2 Constant-fraction trigger gives the same round-trip-times (Δt1 = Δt2) ✔ Copyright © Hamamatsu Photonics K.K.

33. Slide 33: ToF LiDAR challenges: photodetector

    ToF LiDAR challenges: photodetector APD is the most commonly used photodetector ✔ Gain up to ~100 (ok, but not great) ✔ High quantum efficiency ✗ Large excess noise Could SiPM be the detector of choice? Copyright © Hamamatsu Photonics K.K. All Rights Reserved 33

34. Slide 34: ToF LiDAR challenges: photodetector

    ToF LiDAR challenges: photodetector SiPM VBIAS ~ 10’s V; a few volts above breakdown voltage Output of SiPM: Superposition of RQ current pulses … Rf APD Vout SiPM is an array of microcells connected in parallel. Each is a series combination of APD in Geiger mode and quenching resistor. Copyright © H

35. Slide 35: SiPM

    SiPM Photon detection efficiency [%] Gain Overvoltage [V] Wavelength [nm] ✔ Gain (105 – 106) F≈1.3 ✔ ✓Photosensitivity at 905 nm Copyright © Hamamatsu Photonics K.K. All Rights Reserved 35

36. Slide 36: SiPM

    SiPM Output current [A] Amplitude [mV] Time [ns] Incident light level [W] ~ Recovery time ~ Linearity Copyright © Hamamatsu Photonics K.K. All Rights Reserved 36

37. Slide 37: Types of ToF LiDAR

    LiDAR Types of ToF LiDAR Copyright © Hamamatsu Photonics K.K. All Rights Reserved 37

38. Slide 38: ToF LiDAR: Mechanical scanning

    ToF LiDAR: Mechanical scanning scene object scene object pulses of light ω Velodyne LiDAR system: 64 channels (beams) 905 nm, 1.3 or 2.2×106 points per second, 5 - 20 Hz rotation, APD photo- sensors. Copyright © Hamamatsu Photonics K.K. All Rights Reserved 38

39. Slide 39: ToF LiDAR: Rotating multi-facet mirror

    ToF LiDAR: Rotating multi-facet mirror top view ω ≈ 10 rev/s to target six-facet polygonal mirror; each mirror has a different 2D detector tilt angle focusing mirror side view timing & laser diode processing ~100 k pulses/s, λ = 870 nm Reference: Niclass et al. 2014 Copyright © Hamamatsu Photonics K

40. Slide 40: ToF LiDAR: Rotating multi-facet mirror

    ToF LiDAR: Rotating multi-facet mirror 3D map in full daylight Sensor: SPAD 2D array 10 frames/s FOV: 55°×9° Reference: Niclass et al. 2014 Copyright © Hamamatsu Photonics K.K. All Rights Reserved 40

41. Slide 41: ToF LiDAR: Scanning with MEMS mirrors

    ToF LiDAR: Scanning with MEMS mirrors detector timing circuit Light projector, Light source with MEMS mirrors Copyright © Hamamatsu Photonics K.K. All Rights Reserved 41

42. Slide 42: Light projectors: MEMS mirrors

    Light projectors: MEMS mirrors mirror g(θ) fixed electrodes Electrostatic actuation MEMS mirror Magnetic actuation MEMS mirror Combining electrostatic and magnetic actuations allows 2D scanning (two axis rotation). Copyright © Hamamatsu Photonics K.K. All Rights Reserved 42

43. Slide 43: Light projectors: MEMS mirrors

    Light projectors: MEMS mirrors θmax – Total scan angle δθ – Beam divergence (produced by the mirror ✖) N = θmax/δθ – Number of resolvable incident laser beam spots (resolution) MEMS mirror Reference: Patterson et el. 2004 Copyright © Hamamatsu Photonics K.K. All Rights Reserved 43

44. Slide 44: Light projectors: MEMS mirrors

    Light projectors: MEMS mirrors ✔ Low cost ✔ Almost no moving parts ✖ Limited field of view ~ Size/frequency tradeoff ~ Frequency/beam divergence tradeoff Copyright © Hamamatsu Photonics K.K. All Rights Reserved 44

45. Slide 45: Flash LiDAR

    Flash LiDAR APD or SPAD array detection optics timing circuit scene object short (10’s ns) pulse of illumination illumination optics Copyright © Hamamatsu Photonics K.K. All Rights Reserved 45

46. Slide 46: Flash LiDAR

    Flash LiDAR ✔ Resolution limited by the detector ✔ No moving parts ✖ Small field of view ✖ Starved for photons, limited range Copyright © Hamamatsu Photonics K.K. All Rights Reserved 46

47. Slide 47: Optical phase array (OPA)

    Optical phase array (OPA) Far-field radiation pattern Optical Phased Array Each element (pixel, ~30×30 μm2) receives and re-emits light with changed phase and amplitude. Due to interference, the emitted far field radiation can be shaped into variety of patterns, for example beams. Copyright © Hamama

48. Slide 48: Optical phase array (OPA)

    Optical phase array (OPA) ✔ No moving parts ✖ Lobes and beam divergence ✖ Slow (due to cell tuning) Figure from Abediasl & Hashemi 2015 Copyright © Hamamatsu Photonics K.K. All Rights Reserved 48

49. Slide 49: Another approach?

    Another approach? Designing ToF LiDAR at reasonable cost is very challenging What about a different approach borrowed from radar technology? Frequency modulated continuous wave (FMCW) LiDAR Copyright © Hamamatsu Photonics K.K. All Rights Reserved 49

50. Slide 50: Advantages of FMCW LiDAR

    Advantages of FMCW LiDAR • Photon shot noise limited detection • Immune to photon background • Distance and velocity information in frequency domain • Lower-bandwidth electronics Copyright © Hamamatsu Photonics K.K. All Rights Reserved 50

51. Slide 51: FMCW Radar

    FMCW Radar f0 fmax f0 fmax B frequency t=0 t=T t = 2T t = 3T time Chirp-modulation (triangular) of frequency Copyright © Hamamatsu Photonics K.K. All Rights Reserved 51

52. Slide 52: FMCW Radar

    FMCW Radar f(t) Δt fmax fB2 B fB1 received f0 transmitted fD T 2T time 2BR 2Vr 2BR 2Vr fB1 = − fB2 = − − cT λ0 cT λ0 Copyright © Hamamatsu Photonics K.K. All Rights Reserved 52

53. Slide 53: FMCW Radar

    FMCW Radar Δt f1,max frequency Larger bandwidth gives better distance resolution B1 Δf1 f2,max Δt B2 Δf2 f0 T time Copyright © Hamamatsu Photonics K.K. All Rights Reserved 53

54. Slide 54: FMCW Radar

    FMCW Radar fmax Shorter T gives better frequency Δf1 resolution B Δf2 f0 Δt T1 T2 time Copyright © Hamamatsu Photonics K.K. All Rights Reserved 54

55. Slide 55: FMCW LiDAR (heterodyne optical mixing)

    FMCW LiDAR (heterodyne optical mixing) M M frequency fLO shifter detector fPO= fLO+foffset to target M M tunable BS laser optical mixing fa = fLO+foffset + Δf occurs on the M collimator returned light detector receiving optics Copyright © Hamamatsu Photonics K.K. All Rights Reserved 55

56. Slide 56: FMCW LiDAR (heterodyne optical mixing)

    FMCW LiDAR (heterodyne optical mixing) |Etot|2 = |Ea + ELO|2 = |Aacos(2πfat + φa) + ALOcos(2πfLOt + φLO)|2 |Etot|2 = |Ea|2 + |ELO|2 + AaALOcos[2π(fa – fLO)t + (φa – φLO)] BS PD fLO Psig = Pa + PLO + 2√PaPLOcos[2π(fa – fLO)t + (φa – φLO)] ηePsig isig = = ia + iLO + 2√iaiLOcos[2π(fa – fLO)t + (φa – φL

57. Slide 57: Coherent detection

    Coherent detection detector surface For maximum signal: • the beams must overlap (ideally g = 1) g • wavefronts must have the same shape Optical mixing of two beams; • polarization is the same g– overlap factor • spatial coherence Copyright © Hamamatsu Photonics K.K. All Rights Reserved 57

58. Slide 58: Coherent detection

    Coherent detection S i2 gηPs = (i )2 ≈ N SN 2hfB g – overlap factor; η – photodetector quantum efficiency; B – detection bandwidth By making PLO large enough, one can make the detection photon- shot noise limited. Photodiode can be used for the photodetection. Copyright © Hamamatsu Photonics K.K. Al

59. Slide 59: Balanced detection

    Balanced detection Excess noise of LO (through the DC part) can reduce S/N. Remedy: use balanced detection. I1 I1 – I2 I1 = (ηe/hf)[½Plo + ½Ps + (PsPlo)½sin(Δωt + φ)] I2 I2 = (ηe/hf)[½Plo + ½Ps - (PsPlo)½sin(Δωt + φ)] signal +/- are due to π/2 shifts when 50/50 BS light reflects from the BS Local os

60. Slide 60: Balanced photodiodes by Hamamatsu

    Balanced photodiodes by Hamamatsu Amplifier Mon - INPUT - I/V Amplifier Amplifier OUT INPUT + Amplifier Mon + Balanced photodiode module offered by Hamamatsu Hamamatsu also offers matched bare photodiodes Copyright © Hamamatsu Photonics K.K. All Rights Reserved 60

61. Slide 61: Coherent detection: working example

    Coherent detection: working example B = 4.3 GHz λ = 1549.54 nm Gao & Hui 2012 Copyright © Hamamatsu Photonics K.K. All Rights Reserved 61

62. Slide 62: Is there a perfect LiDAR?

    Is there a perfect LiDAR? LiDAR Range Reliability Cost Size Systems System per car Mechanical Long Good Mid. to high Bulky 1 MEMS based Medium to long Good Low Compact 1 – 4 or more Flash Short Very good Low Compact 1 – 4 or more Optical Phase Array Advantages: solid state design with no moving part

63. Slide 63: Summary & Conclusions

    Summary & Conclusions • Some form of LiDAR is likely to be needed on self-driving car • ToF LiDAR is very challenging to design - Beam steering and photodetection are the most outstanding challenges • There is a growing interest in FMCW LiDAR with optical mixing • There is no default LiDAR design ye

64. Slide 64: Upcoming Webinar (January 2018)

    Upcoming Webinar (January 2018) Silicon Photomultiplier: Operation, Performance, & Optimal Applications Presenter: Slawomir Piatek Host: Laser Focus World Wednesday, January 10, 2018 Copyright © Hamamatsu Photonics K.K. All Rights Reserved 64

65. Slide 65: Visit Booth #521 & Presentations at PW18

    Visit Booth #521 & Presentations at PW18 Development of an InGaAs SPAD 2D array for Flash LIDAR Presentation by Takashi Baba, January 29, 2018 (11:00 AM - 11:30 AM) Development of an InGaAs MPPC for NIR photon counting applications Presentation by Takashi Baba, January 30, 2018 (5:50 PM - 6:10 PM) P

66. Slide 66: Thank you for listening!

    Thank you for listening! • Speaker Contact – Slawomir Piatek ([email protected]) • LiDAR Inquires to Hamamatsu – Jake Li ([email protected]) Copyright © Hamamatsu Photonics K.K. All Rights Reserved 66

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