Machine Learning and High-Throughput Computation-Assisted Precise Synthesis of Quantum Dots for Reliable Neuromorphic Computing
Quantum dot (QD)-based memristors enable precise and energy-efficient neuromorphic computing through atomic-level control over electrical synapse performance. However, the stochastic nature of QD structures results in poor reliability of resistive switching, limiting practical applications. This work presents a data-driven QD synthesis optimization loop that integrates high-throughput density functional theory with machine learning to establish a cross-scale screening platform for precise QD synthesis. By minimizing structural disorder through pure phase, uniform size distribution, and highly preferred orientation, QD-based memristors demonstrate a 57% reduction in switching voltage, a two-order-of-magnitude increase in ON/OFF ratio, and endurance and retention degradation as low as 0.1% over 8.4 × 10^7 s of continuous operation and 10^5 rapid read cycles. The dynamic learning range and neuromorphic computing accuracy improve by 477% and 27.8% (reaching 92.23%), respectively. These findings establish a scalable, data-driven strategy for rational design of QD-based memristors, advancing next-generation reliable neuromorphic computing systems.