Motion recognition, especially the distinction between high-speed and low-speed movements, is a challenging computational task that typically requires substantial resources. The extensive response range required to detect such variations in speed often exceeds the capabilities of traditional CMOS technology. This report introduces a SnS₂-based in-sensor reservoir that offers an effective solution for detecting a variety of motion types at sensory terminals. By leveraging in-sensor reservoir computing, the device excels at classifying different motions across a wide velocity spectrum, providing a novel and promising method for motion recognition. The conductance of SnS₂ channel under light stimulation is governed by the trapping and recombination of photogenerated carriers at the inherent defect states, which contributes to the flexible optically dynamical sensing function of the device to varying illumination times. These attributes make the device versatile for both optical sensing and synaptic emulation. The findings suggest that such a SnS₂-based device could be instrumental in advancing motion recognition capabilities for developing next-generation artificial intelligence systems.

Reconfigurable surface acoustic wave (SAW) phase shifters have garnered significant attention owing to their potential applications in emerging fields such as secure wireless communication, adaptable signal processing, and intelligent sensing systems. Among various modulation methods, employing gate voltage-controlled tuning methodologies that leverage acoustoelectric interactions has proven to be an efficient modulation approach that requires a low bias voltage. However, current acoustoelectric devices suffer from limited tunability, intricate heterogeneous structures, and complex manufacturing processes, all of which impede their practical applications. In this study, we present a novel material system for voltage-tunable SAW phase shifters. This system incorporates an atomic layer deposition ZnO thin-film transistors on LiNbO₃ structure. This structure combines the benefits of LiNbO₃’s high electromechanical coupling coefficient (K²) and ZnO’s superior conductivity adjustability. Besides, the device possesses a simplified structural configuration, which is easy to fabricate. Devices with different mesa lengths were fabricated and measured, and two of the different modes were compared. The results indicate that both the maximum phase shift and attenuation of the Rayleigh mode and longitudinal leaky SAW (LLSAW) increase proportionally with mesa length. Furthermore, LLSAW with larger effective electromechanical coupling coefficients (K_eff²) values exhibits greater phase velocity shifts and attenuation coefficients, with a maximum phase velocity tuning of 1.22% achieved. It is anticipated that the proposed devices will find utility in a variety of applications necessitating tunable acoustic components.

Recurrent neural networks (RNNs) have proven to be indispensable for processing sequential and temporal data, with extensive applications in language modeling, text generation, machine translation, and time-series forecasting. Despite their versatility, RNNs are frequently beset by significant training expenses and slow convergence times, which impinge upon their deployment in edge AI applications. Reservoir computing (RC), a specialized RNN variant, is attracting increased attention as a cost-effective alternative for processing temporal and sequential data at the edge. RC's distinctive advantage stems from its compatibility with emerging memristive hardware, which leverages the energy efficiency and reduced footprint of analog in-memory and in-sensor computing, offering a streamlined and energy-efficient solution. This review offers a comprehensive explanation of RC's underlying principles, fabrication processes, and surveys recent progress in nano-memristive device based RC systems from the viewpoints of in-memory and in-sensor RC function. It covers a spectrum of memristive device, from established oxide-based memristive device to cutting-edge material science developments, providing readers with a lucid understanding of RC's hardware implementation and fostering innovative designs for in-sensor RC systems. Lastly, we identify prevailing challenges and suggest viable solutions, paving the way for future advancements in in-sensor RC technology.