The increasing complexity and size of design space poses significant challenges for
integrated circuit (IC) design. This article discusses the potential of machine learning (ML) …

Artificial intelligence (AI) and machine learning (ML) techniques have been increasingly
used in several fields to improve performance and the level of automation. In recent years …

The utilization of Bayesian methods has been widely acknowledged as a viable solution for
tackling various challenges in electronic integrated circuit (IC) design under stochastic …

Autonomous lander missions on extraterrestrial bodies will need to sample granular material
while coping with domain shift, no matter how well a sampling strategy is tuned on Earth …

An RF integrated circuit design heavily relies upon experienced experts to iteratively tune
the circuit parameters. A Bayesian optimization (BO) method is explored in existing works for …

This paper addresses the problem of constrained multi-objective optimization over black-box
objective functions with practitioner-specified preferences over the objectives when a large …

This brief describes the design and characteristics of a wideband low-noise
transconductance amplifier (LNTA) based on cascode inverter cells for use in surface …

The development of surrogate models based on limited data is crucial in enhancing the
speed of structural analysis and design optimization. Surrogate models are highly effective …

Autonomous lander missions on extraterrestrial bodies need to sample granular materials
while coping with domain shifts, even when sampling strategies are extensively tuned on …

This paper presents SRAM design methodology that aims to minimize power consumption
and/or maximize performance while meeting predefined constraints through Bayesian …