An atomic clock is a type of precision timekeeping device that achieves superior stabilityby referencing its frequency to the transition between energy states of atoms, which are the same everywhere and do not change over time. Relentless effort has been devoted to atomic clocks since their advent in the early 1950s. The most accurate clock today achieves an uncertainty on the order of 10−18 based on optical transitions, over 7 orders of magnitude lower than the first generation atomic clock. A number of applications, such as scientific experiments, navigation and communications, have thus benefited from the development of precision atomic clocks. While advanced atomic clocks meet or even surpass the frequency stability requirement for most applications, they are often bulky, power-hungry and expensive. Few solutions exist that combine the atomic grade accuracy with the size, power and cost of quartz oscillators. These portable precision clocks find application in, for example, seismic data acquisition and underwater navigation. Atomic clocks based on gaseous molecular rotational resonance, which typically falls into the millimeter-wave spectrum, are also known as molecular clocks. They have the potential to fill the gap in highly stable and low-cost frequency standards, due to advancements in the silicon process and circuit design at mm-Wave frequencies. Locking to the rotational resonance in molecular clocks could be achieved exclusively with a set of mm-Wave transceivers. Without the need for microwave cavities, lasers or discharge lamps used in other types of atomic clocks, the size, cost and power of molecular clocks are expected to be lower. In this work, a molecular clock based on the 10 ← 9 transition of the carbonyl sulfide gas is implemented. Techniques to remove the linear baseline in the absorption profile and to reduce the transmitter phase noise are presented. The sources that affect the frequency stability are discussed as well. In addition, a high-power and high-efficiency millimeter-wave oscillator has been designed in CMOS, to address the signal generation difficulty in millimeter-wave transmitters.

The advent of high-precision plasma diagnostics tools has created vast amounts of data to be processed and, as a result, increased demand for tools that automate data analysis. One common task in analyzing plasma data is processing spectrogram data to find structures that correspond to physical processes that have occurred, one such example being the toroidal Alfven eigenmode. Due to the extreme environments of the plasma, the data collected are often noisy, thus making automatic detection of structures in the data difficult.This thesis presents a pipeline that takes in raw data from a channel of an Electron Cyclotron Emission Imaging (ECEI) system, which then detects and processes any excited Alfven eigenmodes. Each detected mode is tagged and individually filtered out in the pipeline. The main feature of the pipeline is a deep denoising model. The deep denoising model is created to combat the noise in the spectrograms and enhance the detected signal. A deep denoising architecture is chosen because of its strong ability to denoise images without blurring, unlike traditional denoising models. The architecture used is a Multi-Wavelet Convolutional Neural Network which was then trained using synthetic data. Synthetic data were used due to limited availability of clean measured ECEI data. When applied to real ECEI data, the denoising model achieves significant background denoising, at the cost of occasional hallucinated or spurious structures. The pipeline, in total, has nine steps to process and filter the raw ECEI data. The steps of the pipeline are: time domain low-pass filtering, short-time Fourier transform, spectrogram denoising, thresholding, morphological thinning, trace clustering, trace separation, filter mask creation, mask filtering, and inverse short-time Fourier transform. The combination of the operations in the pipeline allows for the automatic detection and filtering of signals in the spectrogram with a strong rejection of noise.