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Verified CAS / Academic Author2 Decoded Studies

Prof. YAN Xiaobing

Hebei University

Research Publications & English Decoded Briefs

Showing 2 publications
SCIENCE CHINA Materials2026DOI: 10.1007/s40843-026-4339-7

Optoelectronic Memristors Based on ZnS-Passivated CdZnSe Quantum Dots for Neuromorphic Synaptic Emulation Enabling Information Encryption

Neuromorphic computing demands energy-efficient synaptic devices that emulate biological plasticity. Optoelectronic memristors based on colloidal quantum dots (QDs) offer tunable bandgaps and solution processability, yet suffer from defect-mediated nonradiative recombination and instability. Here, we report ZnS-passivated CdZnSe core/shell QDs as the active layer in memristive devices, achieving enhanced synaptic emulation and information encryption. Time-resolved photoluminescence (TRPL) decay curves were fitted with a tri-exponential function, revealing that ZnS passivation suppresses defect-related trap states, prolonging the average carrier lifetime from 12.3 ns (CdZnSe) to 28.7 ns (CdZnSe/ZnS). The intensity proportion of the fast decay component (τ1 ≈ 1.2 ns) decreased from 45% to 18%, indicating reduced surface trapping. Devices incorporating CdZnSe/ZnS QDs exhibit stable bipolar resistive switching with an ON/OFF ratio exceeding 10^3, endurance of >10^3 cycles, and retention of >10^4 s. Under 365 nm UV illumination, the devices show light-tunable synaptic plasticity, including paired-pulse facilitation (PPF) with a facilitation index of 180% at a 50 ms interval, and transition from short-term to long-term memory. The memristors successfully emulate essential synaptic functions and are employed in a simple encryption scheme, demonstrating the potential of defect-passivated QDs for secure neuromorphic hardware.

SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-3817-3

Ultra-robust Y-doped hafnium oxide ferroelectric memristors for intelligent edge computing

The rapid development of artificial intelligence (AI) and big data-driven edge intelligence applications has created an urgent demand for highly efficient computing hardware. Ferroelectric memristors have emerged as promising candidates for edge hardware due to their multi-level conductance tunability and high integration potential. In this work, we fabricated yttrium-doped hafnium oxide (YHO) memristors with a remanent polarization of ~30 μC/cm2, a multi-level resistive state retention time of approximately 10^5 s, and an endurance of up to 10^9 cycles. Based on this device, we constructed a real-time path-tracking system for intelligent vehicles—which achieves 100% path recognition accuracy—and a traffic sign denoising network optimized for hardware mapping via a hierarchical mixed-precision quantization strategy; this network yields denoised images with a peak signal-to-noise ratio (PSNR) of 27.04 and a structural similarity index measure (SSIM) of 0.80. This work paves an innovative pathway for the practical application of hafnium-based ferroelectric memristors, accelerating the development of highly efficient hardware for edge intelligence.