RESUMO
Antimony selenide (Sb2Se3) has sparked significant interest in high-efficiency photovoltaic applications due to its advantageous material and optoelectronic properties. In recent years, there has been considerable development in this area. Nonetheless, defects and suboptimal [hk0] crystal orientation expressively limit further device efficiency enhancement. This study used Zinc (Zn) to adjust the interfacial energy band and strengthen carrier transport. For the first time, it is discovered that the diffusion of Zn in the cadmium sulfide (CdS) buffer layer can affect the crystalline orientation of the Sb2Se3 thin films in the superstrate structure. The effect of Zn diffusion on the morphology of Sb2Se3 thin films with CdxZn1-xS buffer layer has been investigated in detail. Additionally, Zn doping promotes forming Sb2Se3 thin films with the desired [hk1] orientation, resulting in denser and larger grain sizes which will eventually regulate the defect density. Finally, based on the energy band structure and high-quality Sb2Se3 thin films, this study achieves a champion power conversion efficiency (PCE) of 8.76%, with a VOC of 458 mV, a JSC of 28.13 mA cm-2, and an FF of 67.85%. Overall, this study explores the growth mechanism of Sb2Se3 thin films, which can lead to further improvements in the efficiency of Sb2Se3 solar cells.
RESUMO
The brain's function can be dynamically reconfigured through a unified neuron-synapse architecture, enabling task-adaptive network-level topology for energy-efficient learning and inferencing. Here, we demonstrate an organic neuristor utilizing a ferroelectric-electrolyte dielectric interface. This neuristor enables tunable short- to long-term plasticity and reconfigurable logic-in-memory functions by controlling the interfacial interaction between electrolyte ions and ferroelectric dipoles. Notably, the short-term plasticity of the organic neuristor allows for power-efficient reservoir computing in edge-computing scenarios, exhibiting impressive recognition accuracy, including images (90.6%) and acoustic signals (97.7%). For high-performance computing tasks, the neuristor based on long-term plasticity and logic-in-memory operations can construct all of the hardware circuits of a binarized neural network (BNN) within a unified framework. The BNN demonstrates excellent noise tolerance, achieving high recognition accuracies of 99.2% and 86.4% on the MNIST and CIFAR-10 data sets, respectively. Consequently, our research sheds light on the development of power-efficient artificial intelligence systems.
RESUMO
The classification of critical physiological signals using neuromorphic devices is essential for early disease detection. Physical reservoir computing (RC), a lightweight temporal processing neural network, offers a promising solution for low-power, resource-constrained hardware. Although solution-processed memcapacitive reservoirs have the potential to improve power efficiency as a result of their ultralow static power consumption, further advancements in synaptic tunability and reservoir states are imperative to enhance the capabilities of RC systems. This work presents solution-processed electrolyte/ferroelectric memcapacitive synapses. Leveraging the synergistic coupling of electrical double-layer (EDL) effects and ferroelectric polarization, these synapses exhibit tunable long- and short-term plasticity, ultralow power consumption (â¼27 fJ per spike), and rich reservoir state dynamics, making them well-suited for energy-efficient RC systems. The classifications of critical electrocardiogram (ECG) signals, including arrhythmia and obstructive sleep apnea (OSA), are performed using the synapse-based RC system, demonstrating excellent accuracies of 97.8 and 80.0% for arrhythmia and OSA classifications, respectively. These findings pave the way for developing lightweight, energy-efficient machine-learning platforms for biosignal classification in wearable devices.