Litcius/Paper detail

Tailored Environment-Friendly Reverse Type-I Colloidal Quantum Dots for a Near-Infrared Optical Synapse and Artificial Vision System

Jingying Luo, Xin Tong, Shuai Yue, Keming Wu, Xin Li, Hongyang Zhao, Binyu Wang, Zhuojian Li, Xinfeng Liu, Zhiming M. Wang

2024ACS Nano30 citationsDOI

Abstract

Colloidal quantum dots (QDs) are emerging as potential candidates for constructing near-infrared (NIR) photodetectors (PDs) and artificial optoelectronic synapses due to solution processability and a tunable bandgap. However, most of the current NIR QDs-optoelectronic devices are still fabricated using QDs with incorporated harmful heavy metals of lead (Pb) and mercury (Hg), showing potential health and environment risks. In this work, we tailored eco-friendly reverse type-I ZnSe/InP QDs by copper (Cu) doping and extended the photoresponse from the visible to NIR region. Transient absorption spectroscopy analysis revealed the presence of Cu dopant states in ZnSe/InP:Cu QDs that facilitated the extraction of photogenerated charge carriers, leading to an enhanced photodetection performance. Specifically, under 400 nm illumination, the Cu-doped ZnSe/InP QDs-based PDs presented a broadband photodetection ranging from ultraviolet (UV) to NIR, with a responsivity of 70.5 A W –1 and detectivity of 2.8 × 10 11 Jones, surpassing those of the undoped ZnSe/InP QDs-based PDs (49.4 A W –1 and 1.9 × 10 11 Jones, respectively). More importantly, the ZnSe/InP:Cu QDs-PDs demonstrated various synapse-like characteristics of short-term plasticity (STP), long-term plasticity (LTP), and learning-forging-relearning under NIR light illumination, which were further used to construct PD array devices for simulating the artificial visual system that is available in prospective optical neuromorphic applications.

Topics & Concepts

Quantum dotNanotechnologyMaterials scienceInfraredEnvironmentally friendlyColloidOptoelectronicsOpticsPhysicsEngineeringChemical engineeringBiologyEcologyQuantum Dots Synthesis And PropertiesAdvanced Memory and Neural ComputingNeuroscience and Neural Engineering