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].