A Guide to Building Hybrid Large-Area Sensing Systems: Circuit and Algorithmic Techniques for Scalable Interfaces

Abstract

Large-area sensing systems have the potential to interact with the macroscopic physical world, enabling access to a wealth of information. In order to do so, these systems require both a means to acquire and process large amounts of sensor data. Hybrid large-area sensing systems aim to accomplish this by leveraging the strengths of two complementary technologies: (1) large-area electronics (LAE), which enables dense arrays of diverse transducers on substrates that can be large and flexible; and (2) silicon CMOS, which enables efficient and high-performance instrumentation, computation, and power management. However, a key challenge in hybrid system design lies in the interfacing between the two technologies. More specifically, the viability of hybrid large-area sensing systems depends on the scalability of these LAE-CMOS interfaces.

In this thesis, we explore a variety of methods and architectures which aim to ease this interfacing challenge, by leveraging circuit and algorithmic solutions. First we look at solely circuit-based solutions, analyzing both the advantages and limitations of such approaches. Next, we demonstrate an emerging solution space based on incorporating algorithms in conjunction with circuits. We start by analyzing an algorithmic approach based on frequency hopping, which enables large-scale sensor acquisition. Then we focus specifically on “information-processing-driven” architectures, which aim to transfer information, rather than raw data. We look at four such architectures: (1) an embedded classifier system; (2) a row-by-row random-projection- based compression system; (3) a combinatorial switching-network compression system based on random projections; and (4) an electroencephalogram (EEG) acquisition and biomarker-extraction system using compressive sensing.