Wafer‐Scale Organic Neuromorphic Sensors for Programmable Displaying

Conventional machine vision architectures suffer from intrinsic energy inefficiency and latency bottlenecks due to the physical segregation of sensing, memory, and processing units. Emerging organic neuromorphic devices offer a promising solution, yet concurrently achieving strong photoresponse, ultralow-power operation, and reliable non-volatile memory remains a critical challenge. Herein, we integrate a heterostructured dielectric layer, poly (amic acid) (PAA)/hafnium oxide, into wafer-scale organic neuromorphic devices, significantly enhancing charge carrier mobility, photosensitivity, and memory performance. The optimized devices exhibit a suite of exceptional characteristics: high carrier mobility, ultralow light detection sensitivity (102 nW cm<sup>-2</sup>) with a fast response time (50 µs), minimal energy consumption (53 aJ per spike), long-term memory retention (50 000 s), and robust endurance. We further demonstrate programmable organic thin-film transistors (OTFTs) driving LED arrays, enabling repeatable light/mask-induced pattern writing/erasing and integration of sensing-memory-display functions. Linear Dynamic Range Adjustment (LDRA) simulations reveal ultrahigh dynamic range and superior imaging capabilities of PAA-optimized OTFTs. This hetero-dielectric strategy establishes a universal platform for organic neuromorphic electronics, addressing key gaps in adaptive edge vision systems and human-retina-like interactive meta-displays.

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Publication Details

Journal
Advanced Materials
Published
2026-01-28
DOI
https://doi.org/10.1002/adma.202522289
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Wafer‐Scale Organic Neuromorphic Sensors for Programmable Displaying

Deyang Ji, Zhong Chen, Yuning Li, Tao Deng et al.
Advanced Materials
Advanced Memory and Neural Computing
article

Wafer‐Scale Organic Neuromorphic Sensors for Programmable Displaying

Deyang Ji, Zhong Chen, Yuning Li, Tao Deng, Wenping Hu, Ting Jiang, Jiangtao Xu
article en

Abstract

Conventional machine vision architectures suffer from intrinsic energy inefficiency and latency bottlenecks due to the physical segregation of sensing, memory, and processing units. Emerging organic neuromorphic devices offer a promising solution, yet concurrently achieving strong photoresponse, ultralow-power operation, and reliable non-volatile memory remains a critical challenge. Herein, we integrate a heterostructured dielectric layer, poly (amic acid) (PAA)/hafnium oxide, into wafer-scale organic neuromorphic devices, significantly enhancing charge carrier mobility, photosensitivity, and memory performance. The optimized devices exhibit a suite of exceptional characteristics: high carrier mobility, ultralow light detection sensitivity (102 nW cm<sup>-2</sup>) with a fast response time (50 µs), minimal energy consumption (53 aJ per spike), long-term memory retention (50 000 s), and robust endurance. We further demonstrate programmable organic thin-film transistors (OTFTs) driving LED arrays, enabling repeatable light/mask-induced pattern writing/erasing and integration of sensing-memory-display functions. Linear Dynamic Range Adjustment (LDRA) simulations reveal ultrahigh dynamic range and superior imaging capabilities of PAA-optimized OTFTs. This hetero-dielectric strategy establishes a universal platform for organic neuromorphic electronics, addressing key gaps in adaptive edge vision systems and human-retina-like interactive meta-displays.

Advanced MaterialsVol. 38(13)
Tianjin University of Technology (CN), Tianjin University (CN), Beijing Jiaotong University (CN)
Affordable and clean energy
Openalex Percentile: Top 74%
Advanced Memory and Neural Computing
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