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

Karolina Tran, Han Wang, Meike Pieters, Maria Antonietta Loi (Corresponding author)

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1 Citaat (Scopus)

Samenvatting

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.
Originele taal-2Engels
Artikelnummer2300218
Aantal pagina's8
TijdschriftAdvanced Intelligent Systems
Volume5
Nummer van het tijdschrift11
Vroegere onlinedatum27 aug. 2023
DOI's
StatusGepubliceerd - nov. 2023
Extern gepubliceerdJa

Financiering

The authors would like to acknowledge the financial support of CogniGron ‐ Groningen Cognitive Systems and Materials Center. The work was also supported by the European Union (ERC‐AdvancedGrant, DEOM, 101055097). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. The authors thank A. Kamp and T. Zaharia for technical support, J. Pinna, L. Chen, and L. di Mario for discussions, and W. Talsma for the neuromorphic characterization software. The authors would like to acknowledge the financial support of CogniGron - Groningen Cognitive Systems and Materials Center. The work was also supported by the European Union (ERC-AdvancedGrant, DEOM, 101055097). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. The authors thank A. Kamp and T. Zaharia for technical support, J. Pinna, L. Chen, and L. di Mario for discussions, and W. Talsma for the neuromorphic characterization software.

FinanciersFinanciernummer
Groningen Cognitive Systems and Materials Center
DEOM101055097
Groningen Cognitive Systems and Materials Center
European Commission
European Research Council

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