KEYWORDS: Stereoscopic cameras, LIDAR, Video, Line scan image sensors, Control systems, 3D acquisition, Wavelength tuning, Time of flight imaging, Time of flight cameras, Target detection
Autonomous vehicles and robots, key components of the 4th industrial revolution, must be able to observe their 3D environment in realtime. Currently, the 3D video camera using time-of-flight imaging is the most viable solution however raster speeds are currently limited by the speed of mechanical scanners or by the wavelength tuning speed of pulsed lasers. By adapting techniques from ultrafast time-stretch imaging, a new Lidar platform scans orders of magnitude faster than today’s commercial line-scanning pulsed-Lidar systems.
KEYWORDS: Silicon, Energy harvesting, Absorption, Photodetectors, Signal processing, Data acquisition, Signal to noise ratio, Analog electronics, Quantization, Signal detection
The recently proposed nonlinear optical processing method named Optical Dynamic Range Compression (ODRC) has been shown to reduce the burden on the dynamic range in photodetection and data acquisition and enhance the signal-to-noise ratio. Signals with a large dynamic range are compressed by the analog optical dynamic range compressor through a logarithmic-like transform before the photodetection and digitization and then recovered digitally. This powerful idea enables a larger detectable range, optical non-uniform quantization and signal statistics redistribution.
To compress the high-dynamic-range optical signals, ODRC devices should have a nonlinear gain/loss that decreases /increases as input amplitudes increase, and is able to respond to the instantaneous power change. In this work, we explore the extension of this idea to create an optical limiter that harvests the optical power that would otherwise be wasted by the action of the limiter.
In silicon nano-scale waveguide, two-photon absorption (TPA) and the induced free-carrier absorption (FCA) generate nonlinear absorption and limit the output power when the input power increases. The limiting curve can be sharpened to take effect at lower input power by introducing saturated Raman amplification in the forward propagation direction. Through the two-photon photovoltaic effect, the free carriers generated through the two-photon absorption are recycled so the optical energy lost in the compression is harvested as electrical power. The limiting transform can be tuned with the input pump power and the biasing voltage to best fit the statistics of the input signal.
In applications where the device bandwidth is a concern, we showed that the carrier lifetime can be significantly reduced to tens pico-second through the carrier sweep-out with reverse bias.
Photonic time-stretch has established world’s fastest real-time spectrometers and cameras with applications in biological cell screening, tomography, microfluidics, velocimetry and vibrometry. In time-stretch imaging, the target’s spatial information is encoded in the spectrum of the broadband laser pulses, which is stretched in time and then detected by a single-pixel detector and digitized by a real-time ADC, and processed by a CPU or a dedicated FPGA or GPU.
In time-of-flight LiDAR measurement, the maximum detectable distance scales with the temporal duration of the chirped illumination source. The bearing angle is proportional to the bandwidth of the source. In order to have a large detection angle and depth, a large chirp-bandwidth product is required. Various methods have been proposed to generate a chirped output to realize time-stretch, including single mode fibers, dispersion compensating fibers, chirped Bragg grating, and chromo-modal dispersion (CMD). But none of those methods provide the chirped source with a large time-bandwidth product. Moreover, the chirp profile and the operating wavelength can be changed with minimum freedom in those methods.
In this study, we demonstrate the discrete time-stretch method that can generate the giant time-bandwidth product with arbitrary nonlinear chirp at operating wavelength from the visible to the infrared. A chirped pulse train with chirp time-bandwidth product at the order of 106 is easily feasible, rendering time-of-flight imaging of long-ranging distance and large bearing angle possible. We show its application in spectral-temporal LIDAR with the foveated vision at MHz refresh rate.
KEYWORDS: Quantization, Optical signal processing, Signal to noise ratio, Optical amplifiers, Energy harvesting, Silicon photonics, Raman spectroscopy, Orthogonal frequency division multiplexing, Nonlinear optics, Signal processing
Abstract: Optical Dynamic range compression (ODRC) is a new nonlinear signal processing concept that reshapes the dynamic range and signal-to-noise ratio.
ODRC improves the detection sensitivity by boosting weak signals over the noise floor, while keeping the full-scale the same. Without requiring an optical receiver that has post-amplification or higher resolution, the detectable range is extended. For signals whose amplitude distribution is centered at a low value, ODRC effectively reduces the quantization error by assigning more quantization levels to weak signals, reducing the total number of bits to digitize. In amplifiers cascade, a low-noise ODRC serving as the pre-amplifier would relax the noise figure and linear range requirements of the second amplifier.
The silicon photonics industry is projected to be a multibillion dollar industry driven by the growth of data centers. In this work, we present an interactive online tool for silicon photonics. Silicon Photonics Cloud (SiCCloud.org) is an easy to use instructional tool for optical properties of silicon and related materials, waveguide design and modal simulations as well as information capacity of silicon channels.
We present SiCloud (Silicon Photonics Cloud), the first free, instructional web-based research and education tool for
silicon photonics. SiCloud’s vision is to provide a host of instructional and research web-based tools. Such interactive
learning tools enhance traditional teaching methods by extending access to a very large audience, resulting in very high
impact. Interactive tools engage the brain in a way different from merely reading, and so enhance and reinforce the
learning experience. Understanding silicon photonics is challenging as the topic involves a wide range of disciplines,
including material science, semiconductor physics, electronics and waveguide optics. This web-based calculator is an
interactive analysis tool for optical properties of silicon and related material (SiO2, Si3N4, Al2O3, etc.). It is designed to
be a one stop resource for students, researchers and design engineers. The first and most basic aspect of Silicon
Photonics is the Material Parameters, which provides the foundation for the Device, Sub-System and System levels.
SiCloud includes the common dielectrics and semiconductors for waveguide core, cladding, and photodetection, as well
as metals for electrical contacts. SiCloud is a work in progress and its capability is being expanded. SiCloud is being
developed at UCLA with funding from the National Science Foundation’s Center for Integrated Access Networks
(CIAN) Engineering Research Center.
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