FMCW Lidar: Scaling to the Chip-Level and Improving Phase-Noise-Limited Performance


Lidar (light detection and ranging) technology has the potential to revolutionize the way automated systems interact with their environments and their users. Most lidar systems in the industry today rely on pulsed (or, "time-of-flight") lidar, which has reached limits in terms of depth resolution. Coherent lidar schemes, such as frequency-modulated continuous-wave (FMCW) lidar, offer significant advantage in achieving high depth resolution, but are often too complex, too expensive, and/or too bulky to be implemented in the consumer industry. FMCW, and its close cousin, swept-source optical coherence tomography (SS-OCT) are often targeted towards metrology applications or medical diagnostics, where systems can easily cost upwards of $30,000. 

In this dissertation, I present my work in chip-scale integration of optical and electronic components for application in coherent lidar techniques. First, I will summarize the work to integrate a typically bulky FMCW lidar control system onto an optoelectronic chip-stack. The chip-stack consists of an SOI silicon-photonics chip and a standard CMOS chip. The chip was used in an imaging system to generate 3D images with as little as 10um depth precision at stand-off distances of 30cm. 

Second, I will summarize my work in implementing and analyzing a new post-processing method for FMCW lidar signals, called "multi-synchronous re-sampling" (MK-re-sampling). This involved Monte Carlo studies of laser phase noise under non-linear signal processing schemes, so I will show stochastic simulations and experimental results to demonstrate the advantages of the new re-sampling method. QS-re-sampling has the potential to improve acquisition rate, accuracy, SNR, and dynamic depth range of coherent imaging systems.

Publication date: 
December 1, 2017
Publication type: 
Ph.D. Dissertation
Sandborn, P. (2017). FMCW Lidar: Scaling to the Chip-Level and Improving Phase-Noise-Limited Performance. United States: University of California, Berkeley.

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