The emerging optoelectronic memristive synapses having the advantages of both optics and electronics exhibit a great potential in neuro‐inspired computing, which is a new generation of artificial intelligence. Herein, a light stimulated synaptic memristor (LSSM) based on ZnO/Zn₂SnO₄ heterostructure is prepared with the characteristics of reversibly tunable conductance states by varying the wavelength of the incident light. The synaptic feature of this fully photon controlled memristive synapse is revealed by potentiation and depression behaviors stimulated by violet and red light pulses, respectively. Similar to biological brain, the device demonstrates the dynamic learning and forgetting behavior. All‐optically driven and bio‐vision inspired image processing function such as contrast enhancement is exemplified. The international Morse code for Arabic numerals (0–9) is also successfully conveyed by patterned light pulses and suggests the device's potential in the field of optical wireless communication for human–machine interface. Classical Pavlovian conditioning (associative learning) is successfully demonstrated through visible light induction. Finally, the device can realize the recognition application of Zalando's article image through the simulation based on Hopfield neural network (HNN). This work provides a promising approach toward optoelectronic neural systems and human–machine interaction technologies.

While a broadband metamaterial perfect absorber (MPA) has been implemented and proposed intensively in recent years, an ultra-broadband perfect absorber with polarization selectivity has not been realized in literature. In this work, we propose a configuration of polarization-selective (PS) MPA with ultra-wide absorption bandwidth. The aluminum wire grid is integrated on top of the ultrathin-metal-dielectric stacking. The transverse electric (TE) wave is blocked due to the requirement of zero tangential electric field at the metal surface. The transverse magnetic field can pass the aluminum wire-grids because the normal electric field can be supported by the surface charge density at the metal surface, and full absorption of the TM wave is accomplished by the metal-dielectric stacking beneath. Theoretical calculation using rigorously coupled wave analysis demonstrates the wavelength selectivity from λ = 1.98μm to λ = 11.74μm where the TE absorption is 0.95, using 300 nm thick aluminum (Al) wire grid with 16-pair SiO₂/Ti stacking. Additionally, the design is wavelength scalable by adjusting the dielectric thickness (t_SiO2) and the wire grid period (P) and height (t). The experimental result is demonstrated using Al grids and Ti/SiO₂, and the measured result fully supports the calculated prediction.

In comparison to electrical memristive synapses, photoelectric synapses have the capability for ultrafast computing with less power consumption. In this work, a photoelectric synaptic memristor (PSM) based on Zn ₂ SnO ₄ (ZSO)/cuprous oxide (Cu $_{{{2}}}\text{O}$ ) heterostructure is fabricated. This two-terminal PSM device with a simple structure exhibits primary synaptic functions such as long-term potentiation (LTP)/long-term depression (LTD), and spike-timing-dependent plasticity (STDP) by electrical stimulation. Under the exposure of violet light, the observed negative persistent photoconductivity (NPPC) of our PSM is associated with inhibitory synaptic plasticity, which leads to synaptic performances such as short to long-term memory (STM-LTM) transition, learning experience behavior, and a transition from post-tetanic potentiation (PTP) to post-tetanic depression (PTD). Furthermore, this PSM mimics the five essential features of a nociceptive receptor (nociceptor) like threshold, relaxation, no-adaptation, hyperalgesia, and allodynia in response to violet light pulse-induced NPPC phenomenon. These results provide a new path to propel the development of neuro-inspired perceptual systems.

With the immense increase in the processing data during the scaling down of semiconductor devices by Moore’s Law, it is in urgent need to use data analytics to meet the state of the art performance in both manufacturing and device compact modeling. In particular, managing the fabrication cost and promptly providing compact device models, especially for new or emerging devices, is challenging. To ease out these issues, we propose a unified, general-purpose, process-aware machine learning (ML) based compact model (CM) for resistive random-access memory (RRAM), and the same methodology can be used for any memory devices with hysteresis. A long short-term memory (LSTM) ML model is used to fit the RRAM current-voltage (I-V) characteristics. The memorizing capability of LSTM ensures one model can fit both RRAM low resistance state (LRS) and high resistance state (HRS). The fitted dataset is based on the fabricated RRAM samples using TaN/HfO ₂ /Pt/Ti/SiO ₂ /Si structure. The resultant fitting error is 0.0096 in sinusoidal wave input voltage and 0.0148 in random walk voltage sequences. In the process-aware demonstration, we use post-oxide annealing dataset from 300°C to 500°C. The root mean squared error (RMSE) in the process-aware RRAM compact model is 0.0028. Thus, the LSTM-based CM has the potential to compete with the conventional compact device models in terms of shorter developing time, better fitting capability in emerging devices and a large number of devices, easily incorporated process-aware models, and one unified model accounting for LRS and HRS ensuring differentiability. We propose that the LSTM based memory CM can be useful in intelligent manufacturing, process tuning, and simulation program with integrated circuit emphasis (SPICE) modeling in circuit simulation.

Surface plasmon enhancement has been proposed as a way to achieve higher absorption for thin-film photovoltaics, where surface plasmon polariton(SPP) and localized surface plasmon (LSP) are shown to provide dense near field and far field light scattering. Here it is shown that controlled far-field light scattering can be achieved using successive coupling between surface plasmonic (SP) nano-particles. Through genetic algorithm (GA) optimization, energy transfer between discrete nano-particles (ETDNP) is identified, which enhances solar cell efficiency. The optimized energy transfer structure acts like lumped-element transmission line and can properly alter the direction of photon flow. Increased in-plane component of wavevector is thus achieved and photon path length is extended. In addition, Wood-Rayleigh anomaly, at which transmission minimum occurs, is avoided through GA optimization. Optimized energy transfer structure provides 46.95% improvement over baseline planar cell. It achieves larger angular scattering capability compared to conventional surface plasmon polariton back reflector structure and index-guided structure due to SP energy transfer through mode coupling. Via SP mediated energy transfer, an alternative way to control the light flow inside thin-film is proposed, which can be more efficient than conventional index-guided mode using total internal reflection (TIR).