Photovoltaic (PV) technologies have attracted great interest due to their capability of generating electricity directly from sunlight. Machine learning (ML) is a technique for computer to learn how to perform a specific task using known data. It can be used in many areas and has become a hot research topic recently due to the rapid accumulation of data and advancement of computer hardware. The application of ML techniques in the design and fabrication of solar cells started slowly but has recently gained tremendous momentum. An exhaustive compilation of the literatures indicates that all the major aspects in the research and development of solar cells can be effectively assisted by ML techniques. If combined with other tools and fed with additional theoretical and experimental data, more accurate and robust results can be achieved from ML techniques. The aspects can be grouped into four categories: prediction of material properties, optimization of device structures, optimization of fabrication processes, and reconstruction of measurement data. A statistical analysis of the literatures shows that artificial neural network (ANN) and genetic algorithm (GA) are the two most applied ML techniques and the topics in the optimization of device structures and optimization of fabrication processes are more popular. This article can be used as a reference by all PV researchers who are interested in ML techniques.
Organic−inorganic hybrid halide perovskites (OHHPs) offer excellent resistive switching (RS) properties, making them candidates for applications involving low-cost, flexible memories. However, compared with the operational stability of traditional oxide-based RS materials, the operational stability (in terms of endurance and retention) of OHHPs remains an obstacle to their use in RS memories. This paper reports an RS memory with reliable nonvolatile bipolar RS characteristics; the resistive layer is fabricated using a triple-cation perovskite owing to its structural stability and low sensitivity to the atmosphere. These devices offer operational stability over 10 3 endurance cycles and a retention time of up to 10 5 s through an adjustable forming process, which exceeds that of the most previous reports for OHHP-based RS memories with electrodes of Au, graphene, and Al. To better understand the RS mechanism, we simulated the evolution of iodine vacancies using a kinetic Monte Carlo model to elucidate the dynamics of conductive filaments and the device-failure mechanism. The results of this study should improve the stability and increase the understanding of the RS mechanism of OHHP-based memories.
Integrated circuits (ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.
By in-depth analysis of the electrical response of border traps in gate oxide, a new border-trap model is proposed where the ac charging and discharging current associated with those traps is proportional to the variation of the surface potential of semiconductors, resembling the behavior of transconductors. In contrast, the border trap current is directly related to the local potential in the gate oxide in the existing model. The model is then used to provide a qualitative understanding of the temperature-dependent frequency dispersion observed on the Al 2 O 3 /n-GaAs(111)A MOS capacitors at high positive bias.
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