Design of a pipelined time-to-digital Converter (TDC) suitable for transducer array channel multiplexing

Many fields need high performance time measurements, including particle detection, time-resolved imaging, transducer array channel multiplexing and other time of-flight systems. These measurements are often performed by means of time-to-digital converters(TDCs), that for these applications, require high resolution, accuracy, and throughput. This is often accomplished using conventional custom circuitry, which entails low performance and low flexibility. High performance and low cost TDC architectures have been studied to meet the demands of time measurement.This thesis presents a new pipelined TDC with 6.4ps resolution, 1.5LSB integral nonlinearity, and a throughput of 200MS/s. A characterization of the TDC is executed and many influences on performance are described, including transistor distortions, temperature effects and process variation. Some directions for future work are presented, with the possibility to improve pipeline TDCs even more. The results of the pipelined TDC shows the standout Figure of Merit(FOM) of 0.01, and can be used in a wide range of applications requiring high throughput and accurate time measurement.Electrical Engineerin

NASCTY: Neuroevolution to Attack Side-Channel Leakages Yielding Convolutional Neural Networks

Side-channel analysis (SCA) is a class of attacks on the physical implementation of a cipher, which enables the extraction of confidential key information by exploiting unintended leaks generated by a device. In recent years, researchers have observed that neural networks (NNs) can be utilized to perform highly effective SCA profiling, even against countermeasure-hardened targets. This study investigates a new approach to designing NNs for SCA, called neuroevolution to attack side-channel traces yielding convolutional neural networks (NASCTY-CNNs). This method is based on a genetic algorithm (GA) that evolves the architectural hyperparameters to automatically create CNNs for side-channel analysis. The findings of this research demonstrate that we can achieve performance results comparable to state-of-the-art methods when dealing with desynchronized leakages protected by masking techniques. This indicates that employing similar neuroevolutionary techniques could serve as a promising avenue for further exploration. Moreover, the similarities observed among the constructed neural networks shed light on how NASCTY effectively constructs architectures and addresses the implemented countermeasures.Cyber Securit