Xiaoying He

Perovskite-based synapses are regarded as promising candidates for mitigating the von Neumann bottleneck and advancing the field of visual bionics. However, they suffer performance degradation under long-term environmental exposure. Thus, developing a performance-oriented restoration strategy is essential to realize their practical potential. Herein, we fabricate an ion-gel-gated CsPbBr ₃ / graphene synaptic device in which the perovskite is pre-treated with TFSI ⁻ anions to delay performance degradation. After one year tracing in natural conditions, the recognition accuracy declined from 96.13% to 67.8%. To address this, we propose a photoelectronic synergistic modulation strategy to restore performance. By fine-tuning the gate voltage from 5 mV to 10 mV, the photocurrent increases by ∼34.8 % , reaching ∼3.43 μA , and the recognition accuracy is restored to approximately 93.01%, close to its initial level. This approach enhances the photoelectric response and plasticity linearity of the device by promoting rapid transfer of photogenerated holes and suppressing defect-trapping through a transient electric field. This study presents a low-cost, non-reconstructive, and environment friendly functional-recovery strategy, achieves the reactivation from the degradation condition of the synaptic devices, and offers a promising pathway toward stable and controllable neuromorphic hardware systems.

An inverted T-shaped grating coupler with a bottom mirror is proposed and designed on the Si₃N₄-LNOI platform. The impact of structural parameters on the coupler performance is systematically analyzed and discussed. Specifically, the short column parameters can adjust the effective refractive index, while the long column parameters influence the reflection and interaction of the coupled light. A particle swarm optimization (PSO) method is employed to optimize the grating structure, thereby enhancing the peak coupling efficiency and mode matching degree. Through apodization, the peak coupling efficiency is increased from 74% to 81%, and the −1 dB bandwidth is reduced from approximately 127 nm to approximately 117 nm. Additionally, the mode matching degree is improved by 5% via apodization. This work provides valuable support for optical interconnects in photonic integrated circuits on silicon nitride-lithium niobate composite platforms.

With the rapid advancement of artificial intelligence, the energy consumption bottleneck inherent in the von Neumann computing architecture poses a significant obstacle to the future development of edge computing, artificial intelligence, and information technology. Consequently, it is crucial to develop synaptic neural circuits that exhibit memory and learning properties through synaptic plasticity. Drawing inspiration from the side-gated graphene synaptic transistor, we have designed a synaptic neural circuit comprising four key components: pre-voltage input, synaptic weight modulation, electric double-layer effect, and post-membrane current response. Through comprehensive simulations, we have successfully mimicked various synaptic behaviours, including long-term and short-term synaptic plasticity, paired-pulse facilitation, spiking-rate-dependent plasticity, spiking time-dependent plasticity, and Pavlovian associative learning. This approach establishes a robust framework for designing synaptic neural network circuits with advanced learning capabilities, thereby enhancing the practical applications of neural networks and machine learning.

Two typed Bessel-Gaussian beams with good non-diffraction and self-healing properties have been generated by using tapered hollow tubes with double layers. The evolution process of light propagation in the tapered hollow tube cavity is studied theoretically. The theoretically analysis and simulation results show that the Bessel-Gaussian beam generation is due to the multimode interference. Two typed Bessel-Gaussian beams are generated by changing the inner layer thickness of such hollow tubes, which both experiences autofocusing at the output port and evolve into Bessel-Gaussian beams. The effect of single and multiple silica particles on their self-healing performance has also been analyzed, which has a good property for ultrafast laser micromachining.

Exploiting simulation modeling of graphene synaptic field‐effect transistors is extremely important for helping researchers to construct carbon‐based neuromorphic computing systems. Here, lateral‐ionic‐gated graphene synaptic FETs with different gate lengths are fabricated, and they are modeled by using basic physic models combined with the ions migration‐diffusion model and graphene material model. The feasibility and accuracy of the proposed modeling are validated by showing an excellent agreement between simulations and experimental results. The slicing technique of the modeling is proposed to analyze the influence of ionic concentration and diffusion coefficient on the ions movement to reveal their working mechanism. The effect of key parameters about gate length, ionic concentration, and diffusion coefficient on synaptic behavior such as short‐term plasticity, and long‐term plasticity is simulated and discussed. In addition, three kinds of spike‐timing‐dependent plasticity are obtained by the device modeling. This research opens up promising avenues for the development of artificial synapse modeling and paths to new opportunities for the construction of carbon‐based neuromorphic networks.

Time response characteristics of α particle detector are crucial for monitoring radiation fields varied with time and its characterization of pulse radiation field. Here, SRIM-informed TCAD simulation is utilized to visually investigate the transient behaviors of carriers and alpha particles in 4H-SiC Schottky barrier detectors for the time response characteristics. We identified external bias voltage and incident particle energy as key factors to influence transient current pulse broadening. Low-energy alpha particles result in low initial kinetic energy of the ionization-generated carriers, leading to transient current broadening and reduced time resolution characteristics. Conversely, high-energy alpha particles ionize carrier with high drift velocity, preventing the broadening effect. Our simulation provides a planform and valuable guidance for optimizing alpha particle detector, selecting appropriate bias voltages, and enhancing time resolution capabilities.

By combining the good charge transport property of graphene and the excellent photo-carrier generation characteristic of perovskite nanocrystal, we propose and demonstrate an ionic-gated synaptic transistor based on CsPbBr₃/graphene heterojunction for bipolar photoresponse. Controlling the potential barrier of the CsPbBr₃/graphene heterojunction by the ionic-gate of the electrical double-layer effect can promote the separation of photogenerated carriers and effectively retard their recombination. Using the ionic-gate-tunable Fermi level of graphene and the pinning effect of perovskite nanocrystal, the bipolar photocurrent responses corresponding to the excitatory and inhibitory short-term and long-term plasticity are realized by adjusting the negative gate bias. A series of synaptic functions including logic operation, Morse coding, the optical memory and electrical erasure effect, and light-assisted re-learning have also been demonstrated in an optoelectronic collaborative pathway. Furthermore, the excellent optical synaptic behaviors also contribute to the handwritten font recognition accuracy of ∼95% in artificial neural network simulations. The results pave the way for the fabrication of bipolar photoelectric synaptic transistors and bolster new directions in the development of future integrated human retinotopic vision neuromorphic systems.

With the development of neuromorphic electronics, much effort has been devoted to expand perception, memory, and computing integration capabilities. In this paper, an ionic-based graphene synaptic transistor with long-gate structure has been investigated to mimic memory, learning function and perceive humidity. By harnessing the tunable in-plane-field transport of charge carriers in graphene and ions motion in ion-gel, this transistor mimics various synaptic functionalities, including inhibitory postsynaptic current, excitatory postsynaptic current, paired-pulse facilitation, long-term depression, and long-term potentiation. Under short pules stimuli, the long-gate structure provides our transistor with an inertial assisted re-accumulation, generating two excitatory postsynaptic current peaks and enhanced paired-pule facilitation up to ∼265%. Furthermore, the presence of the long-gate structure enables our transistor to exhibit excellent learning and simulate Ebbinghaus' memory. In addition, physical mechanic about its humidity perception has been analyzed and discussed. This study provides a unique platform for designing high-performance carbon-based artificial synapses enabling integrated functions of sensing, storage, and computation for the neuromorphic system.

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed 《中国光学》的前身是1985年创办的《中国光学与应用光学文摘》，当时是国内光学领域唯一的检索刊物。2008年底《中国光学与应用光学文摘》转型更名为《中国光学与应用光学》期刊，刊载内容突破了文摘的限制，正式成为学术刊物。