Information-processing-driven interfaces in hybrid large-area electronics systems

In the development of human-centric systems, access to a large number of human information signals is required. Such signals can be acquired from both ambient and on-person (wearable) sensors. Large-area electronics (LAE) provide distinct capabilities for creating the required diverse, distributed and conformal sensors. However, the large volume of and complex correlation to target information within the captured data requires significant processing and inference. This makes an LAE-CMOS hybrid system well-suited to such applications. Interfacing between the two technologies is a challenge in hybrid system design. We demonstrate an emerging solution space based on information-processing-oriented interfaces, through two case studies: 1) an image sensing and compression system based on random projection [1]; 2) an electroencephalogram (EEG) acquisition and biomarker-extraction system using compressive-sensing circuits [2].

I. Introduction

In recent years, there has been significant progress in the development of wearable technology, driven by applications ranging from medical and wellness, to augmented/virtual reality (A/VR), to advanced human-computer interfaces (HCIs). While the applications span across many different areas, their core objective remains the same: to generate a rich user experience derived from a deep understanding of human intention/function. To develop such a knowledge, these human-centric systems require access to large amounts of contextual information. Thus, the design of such systems is heavily influenced by the ability to sense the wide-range of relevant human signals.