AI Article Synopsis

  • - The text discusses the creation of neuromorphic hardware that can detect and recognize moving targets, contributing to advanced edge computing systems that perform distributed computation.
  • - It highlights the development of an organic synaptic transistor that responds to near-infrared (NIR) light by using upconversion quantum dots, enabling it to sense both visible and non-visible light, which is vital for tracking objects in various environments.
  • - The synaptic transistor mimics biological synaptic functions in response to NIR light and shows promise for dynamic trajectory recognition, particularly in dim conditions, offering advantages over existing technologies, such as improved sensitivity and efficiency for intelligent image recognition systems.

Article Abstract

The development of neuromorphic hardware capable of detecting and recognizing moving targets through an in-sensor computing strategy is considered to be an important component of the construction of edge computing systems with distributed computation. In addition to responsiveness to visible light, the implementation of neuromorphic hardware should also demonstrate the ability to sense and process nonvisible light, which is essential for tracking target object trajectories in specialized environments. In this work, we fabricated an organic synaptic transistor with a near-infrared (NIR) response by incorporating doped LaF: Yb/Ho upconversion quantum dots (UCQDs) into the channel of a Poly3-hexylthiophene (P3HT)-based organic field effect transistor (FET), serving as charge trapping and infrared sensing sites. The obtained synaptic transistor not only replicates common synaptic behaviors when exposed to NIR illumination but also demonstrates potential applications for the dynamic trajectory recognition of animals in the dark. Compared to other monitoring technologies, P3HT transistors doped with LaF: Yb/Ho UCQDs exhibit distinct advantages, including a NIR response, high-efficiency computing, and sensitivity, which provide an experimental foundation and a design reference for the development of next-generation intelligent dynamic image recognition systems.

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Source
http://dx.doi.org/10.1021/acs.jpclett.4c02238DOI Listing

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