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 th...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 the poor reliability of resistive switching in neuromorphic computing,limiting its practical applications.Here,we present a data-driven QD synthesis optimization loop to precisely engineer QD structures for reliable neuromorphic computing.By deeply integrating high-throughput density functional theory with machine learning,we establish a cross-scale screening platform for precise synthesis of QDs,enabling multi-dimension predictions from atomic-level structures to macroscopic electrical synaptic behaviors.Through the minimization of structural disorder,achieved by 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 the 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.Furthermore,the dynamic learning range and neuromorphic computing accuracy are improved 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 the development of next-generation reliable neuromorphic computing systems.展开更多
基金supported by the National Natural Science Foundation of China(51572205,52372159)the Natural Science Foundation Innovation Research Team of Hainan Province(524CXTD431)+1 种基金the National Science Fund for Distinguished Young Scholars of Hubei Province(201CFA067)the National Innovation and Entrepreneurship Training Program for College Students(S202510497020,202510497003,and S202510497010)。
文摘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 the poor reliability of resistive switching in neuromorphic computing,limiting its practical applications.Here,we present a data-driven QD synthesis optimization loop to precisely engineer QD structures for reliable neuromorphic computing.By deeply integrating high-throughput density functional theory with machine learning,we establish a cross-scale screening platform for precise synthesis of QDs,enabling multi-dimension predictions from atomic-level structures to macroscopic electrical synaptic behaviors.Through the minimization of structural disorder,achieved by 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 the 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.Furthermore,the dynamic learning range and neuromorphic computing accuracy are improved 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 the development of next-generation reliable neuromorphic computing systems.
