[Introduction]The full name of LiDAR is Light Detection and Ranging (Laser Detection and Ranging), which is a method of sensing distance using laser light. Depending on the application, different wavelengths can be used, but infrared (IR) is the most common.

Most of the time, the human brain is good at inferring relative depth/distance and size of objects, which is a human instinct, especially when driving a vehicle. But imaging systems have a hard time doing this, especially since standard image sensors represent 3D scenes with 2D images. Using two image sensors in a stereoscopic layout similar to the human eye, is able to extract depth data, but has limited ranging accuracy and is affected by ambient light.

Using LiDAR to obtain depth data allows measurements to be performed independent of lighting conditions and eliminate blurring of images, enabling the distinction and understanding of different objects in the scene. Combining the reflected light pulses fired at the object with precise timing measurements, the distance to the object can be calculated.

LiDAR has a wide range of applications in the automotive field, especially for semi-autonomous vehicles with SAE levels of L3~L5, for example, sensing objects around the vehicle; seeing hundreds of meters ahead on the highway. LiDAR is also commonly used in delivery robots and other applications that require autonomous perception. The technology is also widely used in outdoor applications to quickly generate processable 3D depth maps with high accuracy – a process that would take days using traditional surveying techniques.

For example, LiDAR is used in agriculture to measure fields or land, create maps, and assess crop conditions, thereby enabling farmers to model and predict crop yields, as well as select the most appropriate pesticides/fertilizers. For grains stored in silos and liquids stored in storage tanks, simply install a LiDAR on top of the silo/storage tank and the storage can be measured immediately without contact with the contents.

Environmental groups often use LiDAR to assess deforestation, measure coastal erosion or monitor glacier retreat. Furthermore, in these applications, by installing LiDAR on an unmanned aerial vehicle (UAV)/drone, researchers are able to survey remote and inaccessible areas without having to travel in person.

Smart factories use LiDAR on automated guided vehicles (AGVs) to transport raw materials for processing and deliver finished products to shipping areas. When LiDAR is used by robots in smart factories, the full power of LiDAR can be harnessed to help these robots perform tasks precisely and enable them to sense people around them to work in a thoughtful and safe manner.

LiDAR can be used to quickly survey large construction projects such as railways or highways. LiDAR can also act as a safety aid, keeping certain areas safe from unwanted or accidental intrusions. This is significant where hazardous substances are present or where large machines work. LiDAR works effectively in all lighting conditions, meaning it can provide reliable always-on protection in these types of applications.

Types of LiDARs

The most common type of LiDAR is the direct time-of-flight (dToF) system, and the principle behind it is very simple: measure the time it takes for a light pulse to reach the target and return to the sensor. The speed of light is a known physical constant, so calculating the distance between the emitter/detector and the reflective target is straightforward.

Figure 1: dTOF measures the time it takes for light to reach a target and return

The technique typically uses a single very short pulse emitted by a light source, most commonly a laser, which simultaneously activates a precise timer. When a light pulse hits an object within range, it is reflected back to a highly sensitive light sensor, usually arranged in juxtaposition with the laser. Once the return pulse is detected, the timer stops and the time it takes to reach the object and return can be read.

Using the speed of light constant (c) to calculate the distance to the target object (D) is simple as long as you know the time

The dToF method is fast and efficient and can measure multiple echoes, thus enabling detection of multiple objects within the LiDAR field of view. It is capable of long-range and short-range (0.1 m to 300 m) applications and maintains stable high accuracy over the entire range.

Another LiDAR method, called indirect time-of-flight (iToF), also uses continuous light waves from a laser. This method does not directly measure the passing ToF, but rather determines the ToF from the phase difference between the transmitted and received waveforms.

iToF technology is more suitable for relatively short distance measurements (<10 m), especially for indoor applications where light conditions are not as challenging as outdoors, where contrast is often much higher. The technology can only detect the strongest echoes and therefore only single objects.

The third type of LiDAR is frequency modulated continuous wave (FMCW), which is suitable for short-range and long-range ranging. The technique uses a tunable laser to generate continuous light waves that are mixed with reflected light at the detector. This mixing produces a beat frequency between the local waveform and the reflected waveform, from which object distance and directional velocity can be calculated.

While FMCW has both excellent ranging performance and captures directional velocity information, this LiDAR system uses tunable lasers with polarization control and relies on short-wave infrared wavelengths (requiring special semiconductors for both the laser and detectors). The overall cost is greatly increased.

Figure 2: Comparison of LiDAR-based depth sensing methods

“The Great Wavelength Debate”

One of the most controversial topics surrounding LiDAR is which wavelength to use. The use of IR is preferred over visible light because there is much less background IR and the resulting signal-to-noise ratio (SNR) is better, making it easier to detect returning light.

There are a number of suitable wavelengths in the IR spectral range, including near-infrared (NIR) spectroscopy (850 nm, 905 nm, 940 nm) and short-wave infrared (SWIR) spectroscopy (1350 nm, 1550 nm). Deciding which wavelength to use is a key issue in the Great Wavelength Debate. The three most important criteria to consider are the performance of the system, the availability of suitable components, and the overall cost of the system.

The detector is one of the most basic components in any LiDAR system. CMOS silicon-based detectors can detect light in the wavelength range of 400 nm to 1000 nm, so they are sensitive to visible light and NIR light, but not SWIR light. To detect SWIR light, it is necessary to use III/V semiconductors such as InGaAs alloys, which are very expensive compared to silicon.

Component availability is another consideration, especially when it comes to laser transmitters. Edge-emitting lasers (EELs) are gradually being replaced by vertical-cavity surface-emitting lasers (VCSELs), which are easier to package into arrays and are wavelength stable over temperature. While VCSELs are currently less efficient and more expensive, this situation is expected to improve as their applications continue to expand.

Although there are multiple suppliers for SWIR EELs, there is currently only one supplier for SWIR VCSELs and multiple suppliers for NIR VCSELs. Therefore, choosing NIR is more likely to improve the security of the supply chain.

The detection range is important because it increases the available reaction time and thus improves safety. However, too strong a laser can damage the eyes, so IEC 60825 specifies the maximum permissible exposure (MPE) for 1ns laser pulses.

Although NIR must have a lower MPE, if the pulse width is shortened, the laser power can be increased, and due to the use of sensitive detectors, ranging ranges of up to 300 m can be achieved. In good weather, the range of SWIR will exceed that of NIR, but SWIR is more susceptible to the adverse effects of moisture (such as rain or fog), so NIR-based systems will degrade faster than SWIR systems, allowing Provides more consistent performance in all weather conditions.

Based on the above, NIR is generally considered to be the wavelength of choice for automotive LiDAR. NIR allows us to use silicon-based devices instead of more expensive materials such as InGaAs, and perhaps more importantly, the related components are available from multiple suppliers, helping to build a stronger supply chain. While both NIR and SWIR work to ensure eye safety, NIR still meets automotive LiDAR requirements while using lower power lasers.

From a commercial perspective, NIR is much less expensive, and cost has always been an important consideration in automotive applications. A survey by IHS Markit (Amsrud, 2019) shows that lasers and detectors cost about $4 to $20 per channel, while for a similar SWIR system, the cost per channel is about $275. Even with further development and increased capacity, NIR is still expected to be 10-100 times less expensive than SWIR.

LiDAR composition technology

One of the most important elements of any LiDAR system is the sensing element that captures and quantifies the reflected laser light. While a variety of techniques can be used to achieve this, silicon photomultipliers (SiPMs) generally perform best, primarily because of their ability to detect individual photons with high gains on the order of approximately 1,000,000.

Therefore, in recent years, the application of SiPM has become more and more extensive, and it has become the sensor of choice for LiDAR depth sensing applications. Compared to conventional detectors such as avalanche photodiodes (APDs), which not only have much lower gain, but also require integration of the incoming signal, these devices provide the highest SNR performance for long-range ranging under high-contrast conditions. Other advantages include lower power supply bias, better uniformity, and reduced sensitivity to temperature variations, making SiPM an ideal upgrade option for systems using APDs. SiPM is more sensitive and can use small packaged light modules, thus making it easier to integrate LiDAR into vehicles. Since SiPMs are produced in high-volume CMOS processes, these high-performance devices have the lowest detector cost, further driving the popularity of LiDAR.

Onsemi’s ArrayRDM-0112A20-QFN is a 1 x 12 monolithic array with 0.47 mm x 1.12 mm SiPM pixels based on an advanced proprietary RDM SiPM CMOS process developed for high sensitivity to NIR light , capable of achieving an industry-leading Photon Detection Efficiency (PDE) of 18.5% at 905 nm. At this wavelength, the responsivity is greater than 100 kA/W.

Due to the high internal gain of the SiPM, the sensitivity can be reduced to the single-photon level, which, coupled with the high PDE, enables the detection of weak return signals. This allows the LiDAR system to detect low-reflectivity targets at longer distances. Housed in a robust 10 mm x 5.2 mm QFN package, the array can access 12 individual pixels.

Specifically designed for automotive LiDAR systems (including flash, mechanical or MEMS scanning LiDAR), the array is the first to receive AEC-Q102 automotive qualification and has been developed according to IATF 16949. Due to the low cost and high performance of this array, cost-effective long-range LiDAR solutions can be realized to improve the safety and autonomy levels of vehicles.

Summarize

LiDAR is a significant technology because its scanning system can quickly and accurately determine depth, both for single-point scanning and for 3D mapping of objects or large venues.

When planning a LiDAR design, it is critical to decide which IR light wavelength to use. Considering performance, availability of suitable components and commercial factors, NIR is usually the first choice.

In most LiDAR implementations, the laser source may be relatively simple, but the choice of detector has a large impact on system performance. ON semiconductor‘s newest SiPM array offers excellent detection performance and, more importantly, for automotive applications, it is the first AEC-Q102 qualified SiPM detector.

references

Amsrud, P. (25 September 2019).Enabling the Competition for Low-Cost LiDAR Systems[会议报告]. Automotive LiDAR 2019, Detroit, Michigan, USA.