journal article·2023-05

Picoampere Dark Current and Electro‐Opto‐Coupled Sub‐to‐Super‐linear Response from Mott‐Transition Enabled Infrared Photodetector for Near‐Sensor Vision Processing

Mohit Kumar, Seokwon Lim, Jisu Kim, Hyungtak Seo

Advanced Materials· Volume 35· Issue 18
Publisher page

Abstract

Abstract Light‐intensity selective superlinear photodetectors with ultralow dark current can provide an essential breakthrough for the development of high‐performing near‐sensor vision processing. However, the development of near‐sensor vision processing is not only conceptually important for device operation (given that sensors naturally exhibit linear/sublinear responses), but also essential to get rid of the massive amount of data generated during object sensing and classification with noisy inputs. Therefore, achieving the giant superlinear photoresponse while maintaining the picoampere leakage current, irrespective of the measurement bias, is one of the most challenging tasks. Here, Mott material (vanadium dioxide) and silicon‐based integrated infrared photodetectors are developed that show giant superlinear photoresponse (exponent >18) and ultralow dark current of 4.46 pA. Specifically, the device demonstrates an electro‐opto‐coupled insulator‐to‐metal transition, which leads to outstanding photocurrent on/off ratio (>106), a high responsivity (>1 mA W−1), and excellent detectivity (>1012 Jones), while maintaining response speed (τr = 6 µs and τf = 10 µs). Further, intensity‐selective near‐sensor processing is demonstrated and night vision pattern reorganization even with noisy inputs is exhibited. This research will pave the way for the creation of high‐performance photodetectors with potential uses, such as in night vision, pattern recognition, and neuromorphic processing.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Transition Metal Oxide Nanomaterials · Gas Sensing Nanomaterials and Sensors

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Energy Systems Research Ajou University Suwon 16499 Republic of Korea

Department of Materials Science and Engineering Ajou University Suwon 16499 Republic of Korea

Funding

National Research Foundation of Korea

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