Tissue vitality monitoring is a crucial process to preserve patients’ health during post-surgical recovery or to ensure full adaptation and healing of transplanted organs. Among the many local factors for vitality assessment, tissue oxygenation gives an insight into entire tissue recovery and is directly linked to cell metabolism. To be able to assess oxygenation at the cellular level, NADH fluorescence sensing is used due to its contribution to the cellular respiratory cycle and high sensitivity to oxygen concentration. The current devices for NADH fluorescence sensing are limited to external measurements, where they are suitable for hospital use. This raises the need for a device that is implantable and bioresorbable so that it can stay in the body after the surgery and does not require secondary surgery for removal that puts the patient at risk. The goal of this master’s thesis is to introduce the design of a bioresorbable optical filter and photodetector for the measurements of oxygen through the detection of NADH fluorescence. The design consists of an absorption layer, a Fabry-Perot filter, and a wavelength-specific photodetector; where the overall response is designed to have high sensitivity to the emission wavelength of NADH (470nm) and low sensitivity to the excitation of NADH (350nm). ZnO nanoparticles are chosen to be the absorption layer due to their high absorption properties to 350nm and biodegradability, where the optical response is then tested through spectroscopy measurements. For the Fabry-Perot filter, a design with SiO2 and SiNx layers has been created with simulation and tested with optical measurements. The complete design consists of 15 layers and has a total thickness of approximately 1μm. Lastly; for the photodetector, Spectra simulations have been conducted for the determination of the optimal design properties. The choices are then adapted to a mask design for the fabrication, in which the back side etching of the silicon wafer is required for biodegradability and optical performance. The fabrication of the photodetector has not been completed due to the time frame of the project. The measurements on the combination of ZnO and Fabry-Perot filter show that the transmission of 470nm is 2.5-4 times larger than the transmission of 350nm, which is expected to increase to at least an order of magnitude when combined with the photodetector. For future experiments, it is recommended to conduct more tests on the filters and ZnO to ensure repeatability and consistency.

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.