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Updated: Feb 24, 2026

Inkjet Printing All Inorganic Halide Perovskite Inks for Photovoltaic Applications

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Published on: January 22, 2019

In-sensor computing with halide perovskite-based optoelectronic reservoir networks.

Jeroen J de Boer¹, Agustin O Alvarez¹, Moritz C Schmidt¹

¹LMPV-Sustainable Energy Materials Department, AMOLF, 1098 XG Amsterdam, the Netherlands.

|February 23, 2026

Summary

This summary is machine-generated.

This study introduces a multimodal optoelectronic reservoir network using perovskite devices for efficient in-sensor computing. The novel system processes both light and voltage inputs, achieving high accuracy in image and video classification tasks.

Keywords:

halide perovskite in-sensor computing physical reservoir computing

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Low Pressure Vapor-assisted Solution Process for Tunable Band Gap Pinhole-free Methylammonium Lead Halide Perovskite Films

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Published on: September 8, 2017

Area of Science:

Materials Science
Neuroscience
Computer Science

Background:

Physical reservoir computing offers efficient neuromorphic computing for in- and near-sensor applications.
Existing reservoir networks primarily process light or voltage inputs.
Scalable, high-density sensor arrays are crucial for advanced computing.

Purpose of the Study:

To demonstrate a multimodal optoelectronic reservoir network.
To utilize halide perovskite semiconductor devices for processing both voltage and light inputs.
To assess the network's scalability and performance in classification tasks.

Main Methods:

Fabrication of micrometer-sized, asymmetric crossbar devices coated with methylammonium lead iodide (MAPbI3) perovskite film.
Implementation of a multimodal reservoir network architecture.
Utilizing 4-bit inputs and linear readout layers for image and video classification.

Main Results:

The multimodal network successfully processed both voltage and light inputs.
Achieved high mean accuracies: 95.3% ± 0.1% for image classification and 87.8% ± 0.1% for video classification.
Outperformed linear classifier references by 3.1% for images and 14.6% for video.

Conclusions:

The developed perovskite-based reservoir network is effective for multimodal sensory data processing.
Longer retention times enhance classification accuracy in single-mode networks.
Provides guidelines for optimizing experimental parameters for perovskite reservoir computing.