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Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors

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Ferroelectric-Configured In-Sensor Dynamic Computing with 2D Perovskites for Dim Object Recognition.

Jie Liu¹, Fan Du¹, Limin Wu^1,2

¹College of Smart Materials and Future Energy and State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, P. R. China.

Advanced Materials (Deerfield Beach, Fla.)

|December 26, 2025

Summary

This study introduces a novel ferroelectric photovoltaic device for advanced machine vision. It enables robust dim object recognition by dynamically computing in-sensor, improving accuracy in complex scenarios.

Keywords:

bulk photovoltaic effect ferroelectric materials in‐sensor computing object recognition organic–inorganic hybrid perovskites

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Area of Science:

Materials Science
Optoelectronics
Artificial Intelligence

Background:

Machine vision systems struggle with feature extraction from dim objects in complex environments.
Existing optoelectronic methods often lack the dynamic adaptability required for challenging visual tasks.

Purpose of the Study:

To develop a reconfigurable photovoltaic device array for in-sensor dynamic computing.
To enhance the robustness and accuracy of dim object detection in machine vision.

Main Methods:

Direct synthesis of 2D perovskite ferroelectric nanoplates.
Fabrication of graphene/ferroelectric/graphene heterostructures for reconfigurable photovoltaics.
Design of a convolution kernel optoelectronic sensor array leveraging ferroelectric polarization.

Main Results:

Demonstrated switchable ferroelectric polarization for nonvolatile photovoltaic modulation.
Achieved selective amplification of local image pixel differences for edge feature extraction.
Significantly improved dim object detection accuracy and robustness when integrated with a convolutional neural network.

Conclusions:

The ferroelectric-configured photovoltaic device array offers a promising platform for advanced machine vision.
In-sensor dynamic computing enhances machine vision capabilities for low-contrast and dim object recognition.
This approach overcomes limitations of traditional static optoelectronic processing.