RESUMEN
Collaborative perception between a vehicle and the road has the potential to enhance the limited perception capability of autonomous driving technologies. With this background, self-powered vehicle-road integrated electronics (SVRIE) with a multilevel fractal structure is designed to play a dual role, including a SVRIE device integrated into vehicle tires and a SVRIE array embedded into a road surface. The pressure sensing capability and anti-crosstalk performance of the SVRIE array are characterized separately to validate the feasibility of applying the SVRIE in a cooperative vehicle-infrastructure system. It is demonstrated that the SVRIE based on the multi-layered fractal structure exhibits maximum performance in collaborative sensing and interaction between vehicles and road information, such as vehicle motion, road surface condition, and tire life cycle health monitoring. Traditional data analysis methods are often of questionable accuracy. Therefore, a convolutional neural network is used to classify the vehicle and road conditions with accuracy of at least 88.3%. The transfer learning model is constructed to enhance the road surface identification capabilities with 100% accuracy. The accuracies of the vehicle tire motion recognition and tire health monitoring are 97% and 99%, respectively. This work provides new ideas for collaborative perception between vehicles and roadsides.
RESUMEN
2D-piezoelectric materials are attractive for micro-electromechanical systems (MEMS), medical implants and wearable devices because of their numerous exceptional properties. 2D-hybrid organic-inorganic perovskites (HOIPs) have attracted extensive research interest due to their merits of structural diversity, good mechanical flexibility, and ease of fabrication. The electronic energy band, charge density and the elastic properties of 2D-HOIP-[C6H11NH3]2MX4 (M = Ge, Sn, Pb; X = Cl, Br, I) were investigated using first-principles calculations. The excellent piezoelectricity of 2D-HOIP-[C6H11NH3]2MX4 has been analyzed in detail. More importantly, 2D-[C6H11NH3]2MX4 have giant intrinsic positive and negative out-of-plane piezoelectric coefficients under the effect of van der Waals interaction. The d31 and d32 of [C6H11NH3]2SnBr4 are 82.720 pm V-1 and -36.139 pm V-1, respectively, which are among the largest piezoelectric coefficients among all kinds of atomic-thick 2D materials reported. The high flexibility together with the giant out-of-plane piezoelectricity would endow these 2D-HOIP-[C6H11NH3]2MX4 with potential applications in ultrathin piezoelectric cantilever and diaphragm devices.
RESUMEN
Memristors offer a promising solution to address the performance and energy challenges faced by conventional von Neumann computer systems. Yet, stochastic ion migration in conductive filament often leads to an undesired performance tradeoff between memory window, retention, and endurance. Herein, a robust memristor based on oxygen-rich SnO2 nanoflowers switching medium, enabled by seed-mediated wet chemistry, to overcome the ion migration issue for enhanced analog in-memory computing is reported. Notably, the interplay between the oxygen vacancy (Vo) and Ag ions (Ag+) in the Ag/SnO2/p++-Si memristor can efficiently modulate the formation and abruption of conductive filaments, thereby resulting in a high on/off ratio (>106), long memory retention (10-year extrapolation), and low switching variability (SV = 6.85%). Multiple synaptic functions, such as paired-pulse facilitation, long-term potentiation/depression, and spike-time dependent plasticity, are demonstrated. Finally, facilitated by the symmetric analog weight updating and multiple conductance states, a high image recognition accuracy of ≥ 91.39% is achieved, substantiating its feasibility for analog in-memory computing. This study highlights the significance of synergistically modulating conductive filaments in optimizing performance trade-offs, balancing memory window, retention, and endurance, which demonstrates techniques for regulating ion migration, rendering them a promising approach for enabling cutting-edge neuromorphic applications.
RESUMEN
The utilization of Microelectromechanical Systems (MEMS) technology holds great significance for developing compact and high-performance humidity sensors in human healthcare, and the Internet of Things. However, several drawbacks of the current MEMS humidity sensors limit their applications, including their long response time, low sensitivity, relatively large sensing area, and incompatibility with a complementary metal-oxide-semiconductor (CMOS) process. To address these problems, a suspended aluminum scandium nitride (AlScN) Lamb wave humidity sensor utilizing a graphene oxide (GO) layer is firstly designed and fabricated. The theoretical and experimental results both show that the AlScN Lamb wave humidity sensor exhibits high sensing performance. The mass loading sensitivity of the sensor is one order higher than that of the normal surface acoustic wave (SAW) humidity sensor based on an aluminum nitride (AlN) film; thus the AlScN Lamb wave humidity sensor achieves high sensitivity (â¼41.2 ppm per % RH) with only an 80 nm-thick GO film. In particular, the as-prepared suspended AlScN Lamb wave sensors are able to respond to the wide relative humidity (0-80% RH) change in 2 s, and the device size is ultra-compact (260 µm × 72 µm). Moreover, the sensor has an excellent linear response in the 0-80% RH range, great repeatability and long-term stability. Therefore, this work brings opportunities for the development of ultra-compact and high-performance humidity sensors.