TND6341/D
Rev. 4, July − 2023

Direct Time−of−Flight Depth Sensing
Reference Designs© Semiconductor Components Industries, LLC, 2020

July, 2023 − Rev. 41Publication Order Number:
TND6341/D

Direct Time−of−Flight Depth Sensing Reference Designs
Introduction to Depth Sensing
Depth sensing with precise measurements is a requirement for many applications in today’s
markets, including industrial, consumer, and automotive. onsemi has designed a suite of depth
sensing reference designs and evaluation kits to simplify the design process enabling fasterdevelopment and time−to−market of depth sensing solutions for multiple markets.
There are many different methods for depth sensing. Examples of methods using standard
CMOS image sensors are stereo triangulation, phase detection pixels, and structured light.
Stereo Triangulation | Example: Intel RealSense, Subaru EyeSight

Figure 1. Stereo Triangulation
Distance is obtained from triangulation of received light from two different cameras. By
comparing the disparity in position of an object between the images captured by the cameras,
the distance to the object can be calculated.
• ProsG Passive methodG Standard image sensors• ConsG Requires 2x cameras
G Max distance dependent on distance between cameras
G Highly dependent on light conditions
G Computational costwww.onsemi.com
2• Suitable Applications
G Low−cost depth camera
G Indoors – short distance app
Phase Detection Pixels | Example: iPhone Camera AutoFocus

Figure 2. Phase Detection
Distance is obtained for points in the scene with a single camera. The image sensor at the pixel
level calculates depth by using the phase difference of light received by pairs of pixels with lightshields at difference positions, or by using multiple photodiodes under the same microlens.
• ProsG Passive method
G Standard image sensors• ConsG Poor depth resolution
G Highly dependent on light conditions
G Computational cost
G Short distance• Suitable Applications
G Smartphone autofocus

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3Structured light | Example: iPhone Face ID

Figure 3. Structured Light
A pattern of received infrared light is analysed by a camera with a traditional CMOS image
sensor and the distortion is used to calculate depth in the scene.
Distortion of patterns used to obtain 3D shape of objects.
• ProsG Suitable for short distance• ConsG Active method
G Sensitive to ambient light
G Depth error increases with distance
G Not suitable for long distance• Suitable Applications
G Face recognition
LiDAR

Figure 4. LiDAR
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4Light Detection And Ranging allows for superior depth sensing to alternative approaches
thanks to its high depth and angular resolution, and ability to operate in all light conditions owed
to its active approach of using an infrared light transmitter along with a receiver. LiDAR is widely
deployed across many different markets for a variety of applications and use cases, including
automotive, industrial, robotics, and consumer augmented and virtual reality (AR/VR)
applications.
Generally LiDAR refers to the direct Time of Flight (dToF) measurement technique whichcalculates the time delay between a transmitted signal and its return echo(es). Anotherapproach is indirect time−of−flight (iToF). Both approaches can use pulsed or continuousmodulation.
iToF utilizes modulation of image sensor pixels integrating many light pulses in a limited
number of bins, typically 2. The timing of the returned pulse is determined by measuring thecharge in the different bins.

Figure 5. Direct & Indirect ToF Methods
Table 1. TOF METHOD COMPARISON
Parameter

iToF

dToF

Acquisition Speed

Long Integration Time

Fast Acquisition

Range Ambiguity

Yes

NoDetect Multiple Echoes

NoYes

Pixel Count

Large

Smaller

Data Volume

Small

LargerOperation in Strong Ambient LightOKGood

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iToF is suitable for shorter range depth sensing applications and use in indoor environments
and environments without direct sunlight on the sensor.
dToF is suitable for both short− and long−range depth sensing applications. It offers fasteracquisition rates and the ability to measure multiple echoes, allowing for detection of multiple
objects in a return path.
The rest of this paper will focus on the pulsed dToF method which can be achieved witha single measurement or by accumulating multiple measurements per reading.
In single shot mode a short laser pulse is fired from the transmitter as a timer is activated.
When the laser pulse hits an object within the LiDAR system field of view it is reflected back.
The returned pulse is detected at the receiver and the timer is stopped. Half this delta in time
multiplied by the speed of light provides the distance in meters to the detected object.

Figure 6. Single Shot Mode
The worst−case target is one far away with low reflectivity, such as a pedestrian wearing black
clothes. In this case the returned laser pulse may be hard to discriminate above noise sources
such as photons coming from ambient light. A more powerful laser helps to overcome thislimitation but the maximum laser power must be constrained within eye safety limits as outlined
in IEC−60825−1.
In multi−shot mode, the signal−to−noise ratio (SNR) of a LiDAR system is improved bycreating a histogram of the time stamps of the detected laser pulses, which can be used toextract the distance of the object(s). This method extends the maximum depth that the system
can detect.

Figure 7. Multi−shot ModeIn this mode, every single photon detection event is correlated in time. The resulting histogram
contains a noise level from sources such as ambient photons but the real object in the scene

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provides more returns at about the same time value, creating a clear peak in the histogram
which provides the distance to a target.

Figure 8. Histogram PeakIn considering the methods described above, it is apparent that a highly sensitive sensor is
critical to the performance of the LiDAR system. Example of typical sensors used in dToF LiDAR
systems are shown in Figure 3. PIN diodes and Avalanche Photodiodes (APDs) arelinear−mode detectors that provide an output proportional to the amount of incoming light,
requiring a certain accumulation of photons prior to reaching a threshold that can be correlated
as an object reflection. These legacy detectors are fast being replaced with higher performance
sensors which are sensitive down to single photon built on single photon avalanche diodes(SPADs). Examples of these sensors include Silicon Photomultipliers (SiPMs), SiPM Arrays,and SPAD Arrays. At onsemi, these products are manufactured in a CMOS process offeringtight part−to−part uniformity, low voltage operation, and very high gain. These sensor traits aredesirable for low−cost and high−performance LiDAR mass production in large volumes.

Figure 9. dToF Sensors

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The theory outlined so far has described a single point LiDAR system, which can be aneffective range−finding tool for single point measurement. But the same architecture can be
combined with a scanning optoelectronic system to steer the beams of light and create dense
depth point clouds of a scene. Scanning systems also provide more efficient use of laser power,
and can consist of mechanical rotating devices which are larger and higher cost. Thesemechanical beam steering systems are beginning to be replaced with miniaturized systems
including micro−electromechanical systems (MEMS) mirrors, liquid crystal meta−surfaces(LCM), and optical phased arrays (OPA).
It is also possible to get a dense point cloud without the use of any beam steering by using
sensor and emitter arrays together and flashing the scene. Flash−based LiDAR can be usedwith large arrays of SiPMs or SPADs to create true solid−state solutions. Flash LiDARs are
suitable for shorter to medium range applications due to the dispersion of laser power acrossthe scene and eye safety requirements limiting the amount of emitted photons incident on eachpoint in the sensor field of view.
Examples of scanning methods can be seen in the figures below.

Figure 10. Beam Steering Methods

onsemi LiDAR Reference Designs
After the photon return, LiDAR system signal chains can use either Analog−to−Digital
Converters (ADCs) or Time−to−Digital Converters (TDCs) to digitize the detected laserecho(es). ADC−based systems allow for full pulse digitization, which provides additional
information on the target such as its reflectivity which can be inferred by the pulse shape.
However, there are cost and power advantages to the TDC−based method as the discrimination
circuits are relatively simple to implement, and this approach is compatible with narrow pulse

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width lasers. This means that higher peak power can be used per pulse without affecting eye
safety limits.
The reference designs in this paper are based on a TDC signal processing approach withmulti−shot histogram output to extract depth. They have been developed to allow LiDAR
ecosystem customers and partners to leverage onsemi products and system solutions inbuilding their own designs.

SiPM dToF LiDAR Reference Platform
The SiPM dToF LiDAR Reference Platform is available for purchase and is also a fullreference design by onsemi. This is a near turnkey solution of a single−point LiDAR system
containing the laser transmitter, optics and sensor and readout receive chain including FPGA
code to measure depths from 1 m up to 23 m.

Figure 11. SiPM dToF LiDAR Reference Platform
The transmitter uses an OSRAM SPL PL 90_3, 905 nm laser diode to transmit the laser pulses
to a target. Within the transmit lens, a MicroRB−10010−MLP−TR reference SiPM is used todetect the outgoing laser pulse and create the start signal for the TDC.

Figure 12. RB−Series SiPM Sensor
On the receiver, a second MicroRB−10010−MLP−TR SiPM is used to detect the reflected
pulse from the target. An amplification stage ensures that low intensity reflected laser pulses
are detected. The output of the amplifier provides the stop signal to the TDC.
The TDC is implemented within an FPGA. This is used to timestamp the start and stop signals.
Histogram processing takes place on the FPGA to calculate the depth to a given target.
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A graphical user interface is included with the SiPM dToF LiDAR Reference Platform. Thisallows the user to view the histogram data and to configure the laser range finder for single shotor multi−shot mode. The GUI also allows the user to set the SiPM sensitivity by changing its bias
voltage.

Figure 13. User Interface showing Depth Histogram

Figure 14. SiPM dToF LiDAR Reference Platform
The LiDAR Reference Platform can be configured to connect to other sensors within theonsemi IoT platform.
SiPM dToF Reference Platform Includes:• Eval Kit
G SiPM dToF LiDAR Reference Platform
G Universal Power supply & cable
G Software GUI• Reference Design Documentation
G Full description of the design
G Implementation advice
G Interface command library
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10• Package Release
G FPGA code
G Gerber files
G Cadence files
G Eagle files
G Full BOM
Please contact depthsensing_questions@onsemi.com for more information on our SiPM
dToF LiDAR Reference Platform.

16−Channel LiDAR Reference DesignFigure 15. 16 Channel Reference Design Block Diagram
The 16−channel LiDAR Reference Design is suitable for scanning LiDAR applications
including automotive and industrial markets. It consists of a software release package for thedesign files of several printed circuit boards, designed as modular sub−block components:• Power PCB• Laser PCB• Analog Front End (AFE) PCB• Discriminator PCB• TDC/Processing PCB
Each of these sub−blocks of the reference design has been built, tested, and verified. The
design encompasses all electronic functionality for generating the laser pulses, detecting thewww.onsemi.com
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return signal and generating point cloud. Note that the system optics, scanning hardware andmotor control are outside of the scope of this design.
The reference design can be used in its entirety with an optomechanical sub−system to makea complete LiDAR system, or individual blocks can be leveraged as part of a design when used
as intended with onsemi parts.

Figure 16. Physical PCBs
The power PCB covers the SiPM boost converter to step the 12 V input up to 50 V. The SiPM
bias supply can be modified over SPI communication to control its sensitivity. A boost converter
is also used to supply the laser PCB.
The optical transmitter is a single laser emitter @ 905 nm wavelength. This edge emitting laser
source uses a discrete GaN based laser driver to achieve 75 W peak power lase pulse in a 3 nspulse width.
The AFE board contains the detector and the front−end electronics. The detector is theArrayRDM−0116A10−DFN, which is a 16 channel linear array with industry−leading photon
detection efficiency (PDE) of 16% at 905 nm. Each channel of the SiPM array is read out inparallel to provide 16 depth points simultaneously. The detector connects to an amplification
stage, which provides the gain required to observe single photon events.

Figure 17. ArrayRDM−0116A10−DFN
The discriminator PCB contains a comparator for each channel of the SiPM array. The SiPM
signal is compared to a reference threshold to generate the stop signals for the TDC. The
comparator threshold can be controlled via an SPI−controlled digital−to−analog converter(DAC).
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12This reference design features a 16−channel TDC−based readout for digitizing the returnpulses across the one−dimensional detector array simultaneously. The TDC is implemented onan Intel® Cyclone® 10 LP FPGA. This FPGA also generates the timing histogram and 3D point
cloud data, as well as providing system control and timing signals for the laser and motor controlfunctions.
Target LiDAR systems may scan the single laser beam across the field of view to create a highresolution 3D depth map.
Please contact depthsensing_questions@onsemi.com for detailed information on thisreference design.
Additional Features• Fast, high dynamic range TDC with low deadtime• TDC resolution programmable between 31−250 ps• Max TDC range of 150 m (4096 bins)• Serial and USB interfaces (Ethernet coming soon)• Automotive Grade components used where possible
Automotive Requirements
Automotive applications require high performance sensors and devices that can operatereliably in challenging conditions and across wide temperature ranges. To meet thesechallenges and cusomter requirements, onsemi now has several SiPM and SiPM array
products designed specifically for automotive applications. This includes theArrayRDM−011A16−DFN used in this reference design.
Please contact depthsensing_questions@onsemi.com for more details on our SiPM andSiPM Array products.
These are the first automotive−qualified SiPM sensors available on the market today. Toachieve automotive qualification of these products, onsemi has qualified its labs and designcenters to the IATF16949 manufacturing standard. Prior to mass production release, theseproducts undergo qualification stress testing to the applicable Automotive Electronics Council(AEC) standard AEC−Q102 specifically for optoelectronic semiconductors like SiPMs.
16 Channel LiDAR Reference design Includes:• Reference Design Application Note
G Full description of the design
G Implementation advice
G Hardware test results
G Interface command library
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13• Package Release
G FPGA code
G Gerber files
G Cadence files
G Eagle files
G Full BOM

Target Applications
Table 2. TARGET APPLICAITON
Recommended Reference Design
Laser Rangefinder

16−Channel LiDAR Ref Design

Long/Short Range LiDAR

Yes + Scan Mechanism

Yes + Scan Mechanism

Turn Assist/Blind Spot Detection

Yes + Scan Mechanism

Yes + Scan Mechanism

ADAS

Yes + Scan Mechanism

Yes + Scan Mechanism

Fill Level Monitoring

Yes

Yes

Storage Retrieval

Yes

Yes

Stack Height Control

YesYesMeasuring the Thickness of Metal,
Ceramics, Wood etc.

Yes

Yes

Distance Measurement

Yes

Yes

Distance Monitoring

Yes

Yes

Position Feedback in Industrial
Automation

Yes

Yes

Level Control, e.g. in the Packaging
Industry

Yes

Yes

Exact Positioning of Stacker Cranes,
Gantry Cranes, and Conveyors

Yes

Yes

Safeguarding Minimum Distances

Yes + Scan Mechanism

Yes + Scan Mechanism

Verifying Occupancy Conditions

Yes + Scan Mechanism

Yes + Scan Mechanism

Overhang, Gap, andCompartment−occupied Checks

Yes + Scan Mechanism

Yes + Scan Mechanism

Avoidance of Collisions for AGVs,
Suspended Conveyor Systems, orFreely Navigating Platforms

Yes + Scan Mechanism

Yes + Scan Mechanism

Object Detection and Classification

Yes + Scan Mechanism

Yes + Scan Mechanism

Safety/Proximity

Yes + Scan Mechanism

Yes + Scan Mechanism

Security

Yes + Scan Mechanism

Yes + Scan Mechanism

People Counting

Yes + Scan Mechanism

Yes + Scan Mechanism

Application
AUTOMOTIVEINDUSTRIAL/CONSUMER

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Intel and Cyclone are registered trademarks of Intel Corporation in the U.S. and/or other countries.
Intel and the Intel logo are trademarks of Intel Corporation or its subsidiaries.
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