Multi-Factor Modulated Organic Bulk Heterojunction Synaptic Transistor Enabled by Ligand Engineering for Centrosymmetric In-Sensor Computing

Efficient in-sensor computing necessitates linear, bidirectional, and centrosymmetric photoresponse weight updates; however, the realization of these attributes poses a persistent challenge, with most photosensor devices achieving linear analog weight updates while falling short of accomplishing bidirectional and centrosymmetric characteristics. Here, the development of a quantum dot (QD)–based bulk heterojunction synaptic transistor (QBST) with multi-factor modulation through surface ligand engineering of blend QDs is reported. By controlling the charge transmission between QDs and the semiconductor, the QBST device enables tunable fading memory, which transforms linear weight updates in short-chain devices into linear, bidirectional, and unprecedented centrosymmetric optical synaptic responses in long-chain devices. Moreover, through the synergy of chemical and electric factors, the convolutional kernel of QBSTs-based convolutional neural network realizes enhanced recognition for complex noisy fashion-costume images, achieving an impressive 90.3% accuracy in the long-chain device, highlighting the efficiency of centrosymmetric weight updates. The results demonstrate that surface ligand engineering offers a promising approach for customizable synaptic modulation, facilitating energy- and time-efficient in-sensor computing.