ArticleNature communications2023
Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed.
- Photo-Induced Valence Changed Memristor Based on WOSmall (Weinheim an der Bergstrasse, Germany) · 2026Article
- WSAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- All-Optically Controlled Memristive Reservoir Computing Capable of Bipolar and Parallel Coding.Nature communications · 2026Article
- Mechanically Durable Intrinsically Stretchable Neuromorphic Devices via Molecular Microstructure Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Convergence of Soft Electronics and Artificial Intelligence: From Materials to Intelligent Systems.Nano-micro letters · 2026Review
- Amphibian-inspired neuromorphic dynamic vision systems based on ferroelectric field-effect transistor.Nature communications · 2026Article
- Toward Intelligent Sensing Systems: Non-Equilibrium Materials as Platforms for AI-Enabled Autonomous Discovery.Sensors (Basel, Switzerland) · 2026Article
- In-Sensor-Memory Computing for Post-Von Neumann Intelligence: A Perspective.Nano-micro letters · 2026Article
- Organic Transistor-Based Neuromorphic Electronics and Their Recent Applications.Small methods · 2026Review
- Article
- Very-large-scale mimetic optogenetic synapses for physical reservoir computing.Nature communications · 2026Article
- Homogeneous reservoir computing system based on CMOS-compatible optoelectronic synapses and memristors.Science and technology of advanced materials · 2026Article
- In-sensor image memorization, low-level processing, and high-level computing by using above-bandgap photovoltages.Nature communications · 2025Article
- Device-level nonlinearity and temporal memory in optoelectronic reservoir computing.Nano convergence · 2025Review
- Reconfigurable optoelectronic memristive architecture based on doped nanowire array for in-memory parallel perception and computation.National science review · 2025Article
- Optoelectronic polymer memristors with dynamic control for power-efficient in-sensor edge computing.Light, science & applications · 2025Article
- Frontier applications of retinal nanomedicine: progress, challenges and perspectives.Journal of nanobiotechnology · 2025Review
- Molecular-dipole oriented universal growth of conjugated polymers into semiconducting single-crystal thin films.Nature communications · 2025Article
- Seizure detection via reservoir computing in MoSScience advances · 2025Article
- In situ training of an in-sensor artificial neural network based on ferroelectric photosensors.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
In-sensor multi-task learning is not only the key merit of biological visions but also a primary goal of artificial-general-intelligence. However, traditional silicon-vision-chips suffer from large time/energy overheads. Further, training conventional deep-learning models is neither scalable nor affordable on edge-devices. Here, a material-algorithm co-design is proposed to emulate human retina and the affordable learning paradigm. Relying on a bottle-brush-shaped semiconducting p-NDI with efficient exciton-dissociations and through-space charge-transport characteristics, a wearable transistor-based dynamic in-sensor Reservoir-Computing system manifesting excellent separability, fading memory, and echo state property on different tasks is developed. Paired with a 'readout function' on memristive organic diodes, the RC recognizes handwritten letters and numbers, and classifies diverse costumes with accuracies of 98.04%, 88.18%, and 91.76%, respectively (higher than all reported organic semiconductors). In addition to 2D images, the spatiotemporal dynamics of RC naturally extract features of event-based videos, classifying 3 types of hand gestures at an accuracy of 98.62%. Further, the computing cost is significantly lower than that of the conventional artificial-neural-networks. This work provides a promising material-algorithm co-design for affordable and highly efficient photonic neuromorphic systems.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.