Electroluminescent perovskite QD-based neural networks for energy-efficient and accelerate multitasking learning.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41719399.
- Also identified by DOI 10.1126/sciadv.ady8518 and PMC identifier 12922735.
- Licence recorded as CC BY-NC.
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Abstract
The ability of multitasking (MT) learning in neuro-inspired artificial intelligence (AI) systems offers promise for energy-efficient deployment in robotics, health care, and autonomous vehicles. Here, an MT learning framework is established using a dual-output electroluminescent synaptic device array based on a mixed-dimensional stacked configuration with Cs<sub>1-<i>x</i></sub>FA<i><sub>x</sub></i>PbBr<sub>3</sub> (0.00 ≤ <i>x</i> ≤ 0.15) quantum dots. The device concurrently processes postsynaptic current (PSC) and postsynaptic electroluminescence (PSEL) signals, demonstrating stable and adjustable long-term plasticity with ~1000 individual states, along with spike rate-dependent plasticity and paired-pulse facilitation. By synthesizing the update behavior of both PSC and PSEL pathways, the MT framework simultaneously executes classification-regression and classification-image reconstruction tasks. This approach achieves computational speed improvements of up to 47.09 and 29.17% while reducing energy consumption by up to 8.2- and 32.4-fold compared to a combined single-tasking framework and graphics processing unit-based hardware accelerators, respectively. This innovative method emphasizes the potential of dual-output electroluminescent artificial synapse for MT learning applications.