Related papers: Optical Memory, Switching, and Neuromorphic Functi…
Like single perovskites, halide double perovskites (HDP) have truly emerged as efficient optoelectronic materials since they display superior stability and are free of toxicity. However, challenges still exist due to either wide and…
Organic-inorganic semiconductors, which adopt the perovskite crystal structure, have perturbed the landscape of contemporary photovoltaics research. In this Account, we discuss the internal motion of methylammonium lead iodide…
Hybrid organic-inorganic halide perovskites with the prototype material of CH$_{3}$NH$_{3}$PbI$_{3}$ have recently attracted intense interest as low-cost and high-performance photovoltaic absorbers. Despite the high power conversion…
A multilayer perovskite thin film resistive memory device has been developed comprised of: a Pr0.7Ca0.3MnO3 (PCMO) perovskite oxide epitaxial layer on a YBCO bottom thin film electrode; a thin yttria stabilized zirconia (YSZ) buffer layer…
Hybrid organic-inorganic perovskites have drawn significant interest for applications in optoelectronics over the last few years. Despite rapid progress in understanding the photophysics of perovskite, there remains a need for improved…
Metal halide perovskites (MHPs) are low-temperature processable hybrid semiconductor materials with exceptional performances that are revolutionizing the field of optoelectronic devices. Despite their great potential, commercial deployment…
Multiferroic materials have received an astonishing attention in the last decades due to expectations that potential coupling between distinct ferroic orders could inspire new applications and new device concepts. As a result, a new…
Developing materials that can lead to compact versions of artificial neurons (neuristors) and synapses (memristors) is the main aspiration of the nascent neuromorphic materials research field. Oscillating circuits are interesting as…
The atomic scale dynamics of halide perovskites have a direct impact not only on their thermal stability but their optoelectronic properties. Progress in machine learned potentials has only recently enabled modeling the finite temperature…
Double perovskite halides are emerging as promising materials for a wide range of applications, particularly in renewable energy technologies such as solar cell devices, thereby contributing to addressing global energy demands. In this…
Different real-world cognitive tasks evolve on different relevant timescales. Processing these tasks requires memory mechanisms able to match their specific time constants. In particular, the working memory utilizes mechanisms that span…
In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation…
Halide perovskite solar cells based on CH3NH3PbI3 and related materials have emerged as the most exciting development in the next generation photovoltaic technologies, yet the microscopic phenomena involving photo-carriers, ionic defects,…
Lead (Pb) halide perovskites have achieved great success in recent years due to their excellent optoelectronic properties, which is largely attributed to the lone-pair s orbital-derived antibonding states at the valence band edge. Guided by…
Phase segregation is a critical phenomenon that influences the stability and performance of mixed halide perovskite based opto-electronic devices. In addition to the underlying physical mechanisms, the spatial pattern and randomness…
Using optical hardware for neuromorphic computing has become more and more popular recently due to its efficient high-speed data processing capabilities and low power consumption. However, there are still some remaining obstacles to…
Highly efficient information processing in brain is based on processing and memory components called synapses, whose output is dependent on the history of the signals passed through them. Here we have developed an artificial synapse with…
The investigation of emerging non-toxic perovskite materials has been undertaken to advance the fabrication of environmentally sustainable lead-free perovskite solar cells. This study introduces a machine learning methodology aimed at…
A memristor, a two-terminal nanodevice, has garnered substantial attention in recent years due to its distinctive properties and versatile applications. These nanoscale components, characterized by their simplicity of manufacture,…
Driven by machine-learning tasks neural networks have demonstrated useful capabilities as nonlinear hypothesis classifiers. The underlying technologies performing the dot product multiplication, the summation, and the nonlinear thresholding…