• • 57% reduction in switching voltage: QD-based memristors achieved a 57% lower switching voltage compared to baseline devices, directly reducing power consumption in neuromorphic circuits. This matters industrially because lower operating voltages enable dense, energy-efficient compute-in-memory architectures, critical for edge AI deployment where thermal budgets are constrained.
• • Two-order-of-magnitude increase in ON/OFF ratio: The ON/OFF ratio improved by 100×, enhancing signal margin for reliable state discrimination. This is essential for multi-bit storage and analog weight updates in neural networks, where high ratio ensures low bit-error rates during inference and training.
• • Endurance and retention degradation as low as 0.1% over 8.4 × 10^7 s and 10^5 cycles: The devices exhibited exceptional stability, with only 0.1% degradation after 8.4 × 10^7 seconds (≈2.7 years) of continuous operation and 10^5 read cycles. This addresses the reliability bottleneck that has stalled commercialization of QD memristors in safety-critical applications such as autonomous systems and medical implants.
• • 477% improvement in dynamic learning range and 27.8% accuracy gain (92.23%): The dynamic learning range expanded by 477%, and neuromorphic computing accuracy reached 92.23%, a 27.8% improvement. This directly translates to higher fidelity in pattern recognition tasks, enabling practical deployment in real-time edge computing where accuracy and adaptability are paramount.