Electrode‐Dependent and Tunable Sub‐to‐Super‐Linear Responsivity in Mott Material‐Enabled Near‐Infrared Photodetectors for Advanced Near‐Sensor Image Processing

Brain-like intelligence is ushering humanity into an era of the Internet of Perceptions (IoP), where the vast amounts of data generated by numerous sensing nodes pose significant challenges to transmission bandwidth and computing hardware. A recently proposed near-sensor computing architecture offers an effective solution to reduce data processing delays and energy consumption. However, a pressing need remains for innovative hardware with multifunctional near-sensor image processing capabilities. In this work, Mott material (vanadium dioxide)-based photothermoelectric near-infrared photodetectors are developed that exhibit electrode-dependent and tunable super-linear photoresponse (exponent α > 33) with ultralow modulation bias. These devices demonstrate an opto-thermo-electro-coupled phase transition, resulting in a large photocurrent on/off ratio (>10<sup>5</sup>), high responsivity (≈500 A W<sup>-1</sup>), and well detectivity (≈3.9 × 10<sup>12</sup> Jones), all while maintaining rapid response speeds (τ<sub>r</sub> = 2 µs and τ<sub>d</sub> = 5 µs) under the bias of 1 V. This electrode-dependent super-linear response is found to arise from the electron doping effect determined by the polarity of the Seebeck coefficient. Furthermore, the work showcases intensity-selective near-sensor processing and night vision pattern reorganization, even with noisy inputs. This work paves the way for developing near-sensor devices with potential applications in medical image preprocessing, flexible electronics, and intelligent edge sensing.

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Publication Details

Journal
Advanced Materials
Published
2024-10-17
DOI
https://doi.org/10.1002/adma.202410952
Citations
10
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.09
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article

Electrode‐Dependent and Tunable Sub‐to‐Super‐Linear Responsivity in Mott Material‐Enabled Near‐Infrared Photodetectors for Advanced Near‐Sensor Image Processing

You Meng, Johnny Chung Yin Ho, Danfeng Li, Chi Hou Chan et al.
10 citations
Advanced Materials
Advanced Memory and Neural Computing
2.09
article

Electrode‐Dependent and Tunable Sub‐to‐Super‐Linear Responsivity in Mott Material‐Enabled Near‐Infrared Photodetectors for Advanced Near‐Sensor Image Processing

You Meng, Johnny Chung Yin Ho, Danfeng Li, Chi Hou Chan, Pengshan Xie, Quan Quan, Boxiang Gao, Yan Yan, Weijun Wang, Bowen Li, Mingwei Yang, Baojie Chen, Mingqi Ding
article en
10 citations

Abstract

Brain-like intelligence is ushering humanity into an era of the Internet of Perceptions (IoP), where the vast amounts of data generated by numerous sensing nodes pose significant challenges to transmission bandwidth and computing hardware. A recently proposed near-sensor computing architecture offers an effective solution to reduce data processing delays and energy consumption. However, a pressing need remains for innovative hardware with multifunctional near-sensor image processing capabilities. In this work, Mott material (vanadium dioxide)-based photothermoelectric near-infrared photodetectors are developed that exhibit electrode-dependent and tunable super-linear photoresponse (exponent α > 33) with ultralow modulation bias. These devices demonstrate an opto-thermo-electro-coupled phase transition, resulting in a large photocurrent on/off ratio (>10<sup>5</sup>), high responsivity (≈500 A W<sup>-1</sup>), and well detectivity (≈3.9 × 10<sup>12</sup> Jones), all while maintaining rapid response speeds (τ<sub>r</sub> = 2 µs and τ<sub>d</sub> = 5 µs) under the bias of 1 V. This electrode-dependent super-linear response is found to arise from the electron doping effect determined by the polarity of the Seebeck coefficient. Furthermore, the work showcases intensity-selective near-sensor processing and night vision pattern reorganization, even with noisy inputs. This work paves the way for developing near-sensor devices with potential applications in medical image preprocessing, flexible electronics, and intelligent edge sensing.

Advanced MaterialsVol. 36(49)
Kyushu University (JP), City University of Hong Kong (HK), City University of Hong Kong, Shenzhen Research Institute (CN)
Affordable and clean energy
Openalex Percentile: Top 12%
Advanced Memory and Neural Computing
2.09
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