UC Santa Barbara

In the twilight of Moore’s Law, the semiconductor industry grapples with escalating challenges driven by growing process variations and inherent uncertainties in the manufacturing of increasingly miniaturized devices. Traditional electronic design automation (EDA) tools attempt to address this through rigorous simulation, modeling, and optimization. Concurrently, the need for alternative computational paradigms, notably quantum computing, has surged, introducing fresh complexities and challenges. This dissertation primarily addresses the high cost and low efficiency of modeling and simulation, and the difficulties in design optimization under conditions of noisy and expensive simulations in the context of classical circuits and quantum computing. It proposes uncertainty-aware and efficient design automation methodologies in the EDA field andquantum computing.

The first part of this dissertation focuses on the circuit-level uncertainty quantification and optimization of classical circuits. We first propose a tensor regression model as the stochastic circuit performance model under the high-dimensional process variations. Then, we propose a yield-aware stochastic programming approach to optimize the circuit design, balancing the circuit yield and performance. To tackle optimization in the face of shifted variations, we introduce a distributionally robust optimization formulation, which can be efficiently solved using a Bayesian optimization solver.

The second part addresses block-level simulation and optimization of quantum algorithms. Highlighting the difficulty of the simulation, we begin with an exploration of a variational method employed in the classical simulation of quantum algorithms. Given the substantial obstacle noise poses to the practical applications of quantum computing, we extend the idea of distributionally robust optimization to optimize the parameters of variational quantum algorithms, aiming to enhance the reliability of a quantum algorithm under real-time noise.