# Bidirectional Photoadaptive Organic Heterojunction Synaptic Transistors for Accurate Image Recognition in Machine Vision Systems

## TL;DR

This paper introduces bidirectional photoadaptive synaptic transistors that improve image recognition accuracy in machine vision systems, even under poor lighting conditions.

## Contribution

The novel use of bidirectional photoadaptive organic heterojunctions in synaptic transistors for enhanced image processing and high recognition accuracy.

## Key findings

- n–n heterojunctions show unidirectional excitatory responses, while n‐p heterojunctions exhibit bidirectional responses with inhibitory effects.
- Integration with neural networks enhances low-contrast image details and achieves 97.4% recognition accuracy in ten cycles.
- The devices improve image contrast and edge extraction under adverse lighting conditions.

## Abstract

Machine vision systems are crucial in intelligent scenarios, but actual image acquisition is frequently compromised by the inadequate proficiency of photosensors in photoadaptation. Inspired by biological vision, neuromorphic synaptic phototransistors endowed with photoadaptive capabilities have emerged as a prospective strategy. However, most synaptic phototransistors only exhibit unidirectional positive photoresponses, whereas those capable of bidirectional photoresponses offer a greater possibility of accurately capturing images in complex lighting scenes. Herein, bidirectional photoadaptable organic heterojunction synapse phototransistors as sensing and processing units in systems are reported, which facilitate image contrast enhancement and improve image feature extraction under adverse lighting conditions. The bidirectional plasticity transformation of biomimetic neuromorphic synapses is mimicked. Specifically, n–n heterojunctions exhibit a unidirectional excitatory postsynaptic current, whereas n‐p heterojunctions show a bidirectional response with a more prominent inhibitory postsynaptic current. Most interestingly, by integrating the device characteristics into convolutional neural networks and simultaneously optimizing algorithm architecture, the details and edges of low‐contrast images are markedly enhanced, and the accuracy of image recognition is increased to 97.4% within ten cycles. This work serves as a novel idea for the development of high‐performance neuromorphic visual systems, rendering them promising candidates for in‐sensor computing applications.

Bidirectional photoadaptive organic synaptic phototransistors are developed using complementary n–n and n‐p heterojunctions. These devices enable intelligent in‐sensor processing, markedly enhancing image contrast and edge features under adverse lighting, and achieve a recognition accuracy of 97.4% within ten training cycles for machine vision.