Halide perovskite quantum dots photosensitized-amorphous oxide transistors for multimodal synapses

Deployment of novel artificial synapses serves as the crucial unit for building neuromorphic hardware to drive data-intensive applications. Emulation of complex neural behaviour through conventional Si-based devices requires a large number of elements which increases fabrication complexity and bring...

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Bibliographic Details
Main Authors: Periyal, Srilakshmi Subramanian, Jagadeeswararao, Metikoti, Ng, Si En, John, Rohit Abraham, Mathews, Nripan
Other Authors: School of Materials Science and Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/144612
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Institution: Nanyang Technological University
Language: English
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Summary:Deployment of novel artificial synapses serves as the crucial unit for building neuromorphic hardware to drive data-intensive applications. Emulation of complex neural behaviour through conventional Si-based devices requires a large number of elements which increases fabrication complexity and brings challenges of connectivity. Hence, there is a need to investigate alternative material systems and device architectures for emulating richer neural behaviour comprising of lesser elements. Herein, a thin-film transistor (TFT)-like synaptic device using all-inorganic Cesium lead bromide (CsPbBr3) perovskite quantum dots (QDs) and amorphous Indium Gallium Zinc Oxide (a-IGZO) semiconductor active material is explored for brain-inspired computing. The incorporation of CsPbBr3 QDs as a photosensitizer aids in realizing light-dependent synaptic memory. Furthermore, type II heterostructure can serve as a basis for electro-optical programming. The proposed artificial synapse demonstrates a materials combination that could decouple optical absorption and charge transport property, provides freedom to tune the spectral region. Harnessing the advantages of novel materials, our devices obey spike-timing-dependent plasticity rules, inculcate associative learning and linear non-volatile blind updates. This architecture paves way for efficient building of neuromorphic hardware elements with facile tunability and tailorable plasticity.