Type-printable photodetector arrays for multichannel meta-infrared imaging

TL;DR

This paper introduces printable graphene plasmonic photodetector arrays driven by ferroelectric superdomains, enabling multichannel infrared imaging with enhanced edge discrimination, simplified system design, and high classification accuracy.

Contribution

The study presents a novel, printable photodetector array that simplifies multichannel infrared imaging and improves edge detection without complex optical components.

Findings

Photodetectors show multiple spectral responses with zero-bias operation.

Enhanced shape classification accuracy of 98.1%.

Edge detection accuracy of 98.2%.

Abstract

Multichannel meta-imaging, inspired by the parallel-processing capability of neuromorphic computing, offers significant advancements in resolution enhancement and edge discrimination in imaging systems, extending even into the mid- to far-infrared spectrum. Currently typical multichannel infrared imaging systems consist of separating optical gratings or merging multi-cameras, which require complex circuit design and heavy power consumption, hindering the implementation of advanced human-eye-like imagers. Here, we present a novel approach for printable graphene plasmonic photodetector arrays driven by a ferroelectric superdomain for multichannel meta-infrared imaging with enhanced edge discrimination. The fabricated photodetectors exhibited multiple spectral responses with zero-bias operation by directly rescaling the ferroelectric superdomain instead of reconstructing the separated gratings. We also demonstrated enhanced and faster shape classification (98.1%) and edge detection (98.2%) using our multichannel infrared images compared with single-channel detectors. Our proof-of-concept photodetector arrays simplify multichannel infrared imaging systems and hold great potential for applications in efficient edge detection in human-brain-type machine vision.