4.6 Article

Lead Sulfide Quantum Dots for Synaptic Transistors: Modulating the Learning Timescale with Ligands

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ADVANCED INTELLIGENT SYSTEMS
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WILEY
DOI: 10.1002/aisy.202300218

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colloidal quantum dots; field-effect transistors; lead sulfide; ligand exchange; neuromorphic engineering; synaptic transistors

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This article presents the first example of synaptic transistors utilizing colloidal quantum dots (CQDs) as active material. It is demonstrated that PbS-CQDs with metal halide and perovskite-like ligands are ideal for synaptic transistors, exhibiting both short- and long-term plasticity. Interestingly, by changing the chemistry of the quantum dot outer monolayer, a drastic change in the temporal response of learning is observed, demonstrating the possibility of engineering materials with learning at multiple timescales, similar to biological synapses.
One of the emerging paradigms to resolve pressing issues of modern computing electronics, such as limits in miniaturization and excessive energy consumption, takes inspiration from the biological brain and is therefore expected to display some of its properties, such as energy efficiency and effective learning. Moreover, one of the brain's remarkable properties is its ability to process complex information by resolving it on different timescales. In synapse-emulating artificial devices, some form of memory (e.g., hysteresis in current-voltage characteristics) is required. One of the important characteristics of biological synapses is the coexistence of short- and long-term memory, also called plasticity. However, a broader exploration of memory at multiple timescales in materials remains limited. Herein, the first example of synaptic transistors utilizing colloidal quantum dots (CQDs) as active material is reported. It is demonstrated that PbS-CQDs, with metal halide and perovskite-like ligands, are ideal as an active material for synaptic transistors exhibiting both short- and long-term plasticity. Most interestingly, by changing the chemistry of the quantum dot outer monolayer, a drastic change in the temporal response of the learning is observed, demonstrating the possibility of engineering materials exhibiting learning at multiple timescales, similar to the biological synapses.

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