Exploration of High-Dimensional Performance Spaces via Clustering⁎

Abstract

Performance spaces obtain all possible combinations of competing performance parameters for analog integrated circuits, like gain and bandwidth. The best combinations of those performances form the so-called Pareto front. While these spaces contain a lot of information, visualizing them—especially in high dimensions—can be overwhelming and difficult. Therefore, we present a combination of Parallel Coordinates plots together with a clustering algorithm in order to allow the exploration of performance spaces in a simple and intuitive manner. To compute approximations of those high-dimensional spaces, we use a parallelized version of a state-of-the-art box-coverage algorithm. Several numerical simulations for operational transconductance amplifiers demonstrate the functionality and efficiency of our concept for up to eight-dimensional performance spaces.