\chapter*{Introduction}
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\addcontentsline{toc}{chapter}{Introduction}

The invention of the \ac{SPAD} and its integration in standard \ac{CMOS} fabrication processes has enabled rapid growth of a wide range of new imaging applications. The single photon sensitivity and picosecond level temporal jitter of \acsp{SPAD} has been utilized for low illuminance imaging, positron emission tomography, various types of spectroscopy, fluorescence lifetime imaging microscopy, diffuse optical tomography or \ac{DToF} based \ac{LIDAR}.

The \ac{DToF} based \ac{LIDAR} measures the time interval between the instant when a laser pulse was transmitted, illuminating the scene, and the instant the reflected photons have returned and were detected by the receiver. The time interval is then used to calculate the distance to the obstacle. Compared to other 3D imaging and ranging techniques such as ultrasonic sensors, stereo vision cameras or millimetre wave radars, \ac{LIDAR} can provide superior resolution and competitive dynamic range, while requiring less computing power and operating reliably in environments with uncontrolled ambient light or low illuminance conditions. This is achieved by utilizing \ac{TCSPC}, a statistical technique capable of restoring photon arrival times with picosecond resolution.

The superior spatial and longitudinal resolution of \ac{LIDAR} has attracted attention from the automotive industry in particular, as it not only allows the detection of road traffic, but also relatively small obstacles such as pillars, wires or road defects at a distance. This information can be used by the \ac{ADAS}, which incorporates advanced cruise control, autonomous emergency braking, pedestrian detection, forward collision warning etc. As new car assessment programs across the world are expanding their safety rating criteria to include the safety of pedestrians as well as the passengers, it is expected that in the near future, safety systems like \ac{ADAS} will be commonplace in cars across all price points. An even more advanced driving system, autonomous driving, is another application for \ac{LIDAR} technology, as proven by Waymo, which uses a 360° \ac{LIDAR} based computer vision to operate their driverless car prototypes \cite{waymo}.

The main disadvantage of \ac{LIDAR} has historically been its cost and size. To scan the whole scene with a sufficient \ac{SNR}, laser scanning is needed, which requires complex optomechanical systems. The recent efforts are therefore focused on bringing the cost and the size down by integrating the whole scanning system on a single \ac{CMOS} integrated circuit.

A \ac{DToF} measurement system consists of the following main parts: the laser source, the photodetector, the \ac{TDC} and the data processing circuits. The \ac{TDC} measures the time interval between the transmitted laser pulse and its reflected echo, and as such is one of the most critical parts, determining the dynamic range and the resolution of the system. The key challenges of \ac{TDC} design are achieving the sub-nanosecond resolution, and the generation and routing of the counting signals in a power efficient manner.

The \autoref{chapter:lidar_theory} of this thesis will introduce the general principles of \ac{DToF} based \ac{LIDAR} systems, the operation of \acp{SPAD} or the \ac{TCSPC} technique. The \autoref{chapter:tdcs} will focus on \acp{TDC}, their key parameters, practical design techniques and their various implementations. In the \autoref{chapter:ilo_theory}, a novel approach to \ac{TDC} architecture, utilizing \acp{ILO} to achieve power efficient clock generation and resolution on the order of tens of picoseconds will be described. In \autoref{chapter:model}, a \textsc{Matlab} model of an \ac{ILRO} will be presented. Finally, in \autoref{chapter:design}, an \ac{ILRO} will be designed and simulated in a \SI{65}{\nano\meter} \ac{CMOS} processing technology.