Light detection and ranging (LIDAR) is rapidly emerging as a key technology for depth sensing applications. LIDAR acts as an auxiliary ranging technique, such as radar, thermal image ranging, image recognition, and time-of-flight (TOF) sensor. As a special type of direct time-of-flight (D-TOF) system, this work presents the system design and the hardware implementation of a one dimension time-of-flight (1D-TOF) sensor that uses LIDAR with single-photon avalanche diodes (SPADs). The system is designed for consumer electronics, serving for short-distance target ranging in proximity and face detection.
A typical 1D-TOF LIDAR system is composed of a pulsed laser which emits photons, and a sensor which measures the photons that are reflected back from the target. In 1D-TOF LIDAR systems, the following elements are of great importance: SPAD, time-to-digital converter (TDC), TOF histogram, and depth estimation algorithm.
SPAD is the key component of the system, which is designed to detect a single photon. The target ranging can be estimated by measuring the travel time of the photons. With the help of SPADs, the detection of the photons is converted into a response of the SPAD circuity, in which the optical signal is converted to the electrical signal. And, we can measure the moment in which the electrical signal is active to estimate the travel time of the photons.
TDC is a tool for time measurement. In a 1D-TOF LIDAR system, it measures the exact moment, when a SPAD is triggered by a single photon. The measured time is called the timestamp of the photon.
A TOF histogram accumulates the timestamps of the photons at every time interval. In a TOF histogram, we can observe the time distribution of the photons. In essence, the photons come from two sources, noise and signal. With the help of algorithms, we can derive signal information and reject noise information from a TOF histogram. Also, the algorithm enables us to retrieve the TOF information of the histogram, in which the distance between the target and the sensor lies within.
In this work, the basic concepts of the 1D-TOF LIDAR system is described in chapter 1, and a prototype architecture is discussed in chapter 2. Next, the design is decomposed into stages, including system modeling in chapter 3, system trade-off analysis in chapter 4, algorithm design in chapter 5 and hardware implementation in chapter 6. Moreover, we also propose novel approaches both in system level modeling and algorithm design based on artificial intelligence methods.

Diagnostic imaging is a fundamental requirement for the effective treatment of about 25 % of patients globally. Ultrasound imaging is considered the safest, least expensive and most convenient diagnostic imaging modality. Its widespread adoption has been in the area of 2D imaging. However, the accuracy of diagnosis with 2D ultrasound imaging is highly dependent on the experience and expertise of the sonographer because the sonographer has to mentally create a 3D impression from multiple 2D images of the region of interest and this could lead to erroneous diagnosis. 3D ultrasound imaging addresses this concern, however, it drives up the cost of ultrasound imaging. Also, disadvantages such as difficulty in taking quality images, prolonged time in learning how to effectively take these images and human distress during the acquisition of these images have been reported. By leveraging the high level of precision, accuracy and maneuverability provided by robotic systems in conjunction with low-end ultrasound imaging platforms, 3D ultrasound images can be captured, the problem of human distress alleviated and subsequently reducing the cost of 3D ultrasound imaging. This project sought to explore the feasibility of designing a low-cost 3D ultrasound robotic system. The design followed a distributed system approach using ROS (Robotic Operating System) where the data acquisition decoupled from the processing and visualization unit. The performance of the design was tested by constructing 3D ultrasound images of custom made phantoms. These results were compared with 3D ultrasound images of the phantoms acquired using the Philips EPIQ 7 - a high-tier ultrasound machine. The results obtained with this design had a low resolution in comparison with those obtained with the Philips EPIQ 7 however, a 3D point cloud representation of the object embedded in the phantom can be seen. Also, a full 3D image of the phantom could not be acquired due the transducer movement limitations of the design. This design successfully demonstrates the feasibility of integrating low-end electronics with robotic systems to acquire 3D ultrasound images.

High speed imagers find applications in many fields such as scientific and medical imaging, automotive applications, machine vision and much more. In this thesis, the design of a high speed, high dynamic range (HDR) CMOS sensor with electronic global shutter (GS) and flexible exposure control is presented. The sensor is designed in the 0.11μm CIS process, features 1k(H) x 1k(V) pixels and achieves frame rates greater that 10.000 fps.A review of the architecture of the sensor is given, along with functional illustrations for each comprising block. The quadrant-based approach is described, along with the selectable region-of-interest capability. The pixel design is a eleven-transistor (11T) pinned photodiode global shutter pixel, implementing HDR by means of two in-pixel capacitors. The design of the pipelined Sample & Hold, column gain and column-level Correlated Double Sampling (CDS) circuits are shown.

Imaging sensors are remarkable devices which are able to capture moments and present them in a form available to us for years to come. With the use of material properties and photon particles, charges can be produced which, in combination with electric circuitry, transform into information understandable to the human eye and eventually the brain. When a photon particle hits a silicon photodiode, an electron-hole pair forms. By using readout techniques optimized for noise, area and power, images of different quality can be read out. Imaging concepts such as Signal-to-Noise Ratio, Dynamic Range, Resolution, Contrast and more can be used to describe the quality of the sensor. These concepts of quality are of interest when designing an imaging sensor. Throughout this thesis, design methodologies are applied to both the field of Optical Tomography and Neural Networks.

Optical Tomography is an imaging technique capable of detecting the internal structure of a subject with the use of image sensors. Various Optical Tomography techniques exist such as Diffusion Optical Tomography, Optical Projection Tomography and Optical Coherence Tomography (OCT). Optical Coherence Tomography is of special interest due to published papers which show the potential of the use with CMOS image sensors. The technique is based on the Michelson interferometer which is used in order to retrieve data of micrometer resolution. A feasibility study was conducted on OCT which shows potential for future research due to its recent development using CMOS image sensors. A review of state-of-the-art solutions is presented. With the most recent publication on a CMOS image sensor in OCT, with a dynamic range of 66 dB and a frame rate of 730 frames per second, showed the possibility of retrieving OCT images of higher quality compared to conventional sensors.

Artificial Neural Networks are inspired by the complex structure of our brain. By using its unique way of parallel computation, algorithms are capable of teaching our electronic devices human capabilities such as speech and face recognition. Artificial Neural Networks are identified by neurons and synapses in analogy to the nervous system. In this thesis, pixel design concepts are applied to neural networks where the pixel structure is modelled as a neuron. As a part of a larger project, low power design cells are collected into a combined library and presented. The library, in the end of development, will be used for finalizing and optimizing the complete neural network. One of the project’s challenges was to design a novel absolute value filter. The filter’s importance is presented as a part of a learning algorithm where it takes an absolute value of the difference between two signals. The resulting filter is operational and has the low power dissipation of 87 nW. The area was minimized and includes 10 transistors and a current mirror. The signal gain is around -13.2 dB which shows attenuation of the signal. The amount of gain required for this structure, as part of the learning algorithm, is not yet determined. Future work includes improving the circuit’s gain with a gain boost technique and minimizing noise levels with the use of larger transistors and new technologies.