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Towards High Performance Ga2O3 Electronics: Epitaxial Growth and Power Devices Guest Editors: Genquan Han, Shibing Long, Yuhao Zhang, Yibo Wang
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Celebration of the 60th Anniversary of Dedicating to Scientific Research of Prof. Zhanguo Wang Guest Editors: Zhijie Wang, Chao Zhao , Fei Ding
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Reconfigurable Computing for Energy Efficient AI Microchip Technologies Guest Editors: Haigang Yang, Yajun Ha, Lingli Wang, Wei Zhang, Yingyan Lin
AbstractFull TextPDF
Robots are widely used, providing significant convenience in daily life and production. With the rapid development of artificial intelligence and neuromorphic computing in recent years, the realization of more intelligent robots through a profound intersection of neuroscience and robotics has received much attention. Neuromorphic circuits based on memristors used to construct hardware neural networks have proved to be a promising solution of shattering traditional control limitations in the field of robot control, showcasing characteristics that enhance robot intelligence, speed, and energy efficiency. Starting with introducing the working mechanism of memristors and peripheral circuit design, this review gives a comprehensive analysis on the biomimetic information processing and biomimetic driving operations achieved through the utilization of neuromorphic circuits in brain-like control. Four hardware neural network approaches, including digital-analog hybrid circuit design, novel device structure design, multi-regulation mechanism, and crossbar array, are summarized, which can well simulate the motor decision-making mechanism, multi-information integration and parallel control of brain at the hardware level. It will be definitely conductive to promote the application of memristor-based neuromorphic circuits in areas such as intelligent robotics, artificial intelligence, and neural computing. Finally, a conclusion and future prospects are discussed.
Robots are widely used, providing significant convenience in daily life and production. With the rapid development of artificial intelligence and neuromorphic computing in recent years, the realization of more intelligent robots through a profound intersection of neuroscience and robotics has received much attention. Neuromorphic circuits based on memristors used to construct hardware neural networks have proved to be a promising solution of shattering traditional control limitations in the field of robot control, showcasing characteristics that enhance robot intelligence, speed, and energy efficiency. Starting with introducing the working mechanism of memristors and peripheral circuit design, this review gives a comprehensive analysis on the biomimetic information processing and biomimetic driving operations achieved through the utilization of neuromorphic circuits in brain-like control. Four hardware neural network approaches, including digital-analog hybrid circuit design, novel device structure design, multi-regulation mechanism, and crossbar array, are summarized, which can well simulate the motor decision-making mechanism, multi-information integration and parallel control of brain at the hardware level. It will be definitely conductive to promote the application of memristor-based neuromorphic circuits in areas such as intelligent robotics, artificial intelligence, and neural computing. Finally, a conclusion and future prospects are discussed.
AbstractFull TextPDF
Memtransistors in which the source−drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic computing. On the other side, it is known that the complementary metal-oxide-semiconductor (CMOS) field effect transistors have played the fundamental role in the modern integrated circuit technology. Therefore, will complementary memtransistors (CMT) also play such a role in the future neuromorphic circuits and chips? In this review, various types of materials and physical mechanisms for constructing CMT (how) are inspected with their merits and need-to-address challenges discussed. Then the unique properties (what) and potential applications of CMT in different learning algorithms/scenarios of spiking neural networks (why) are reviewed, including supervised rule, reinforcement one, dynamic vision with in-sensor computing, etc. Through exploiting the complementary structure-related novel functions, significant reduction of hardware consuming, enhancement of energy/efficiency ratio and other advantages have been gained, illustrating the alluring prospect of design technology co-optimization (DTCO) of CMT towards neuromorphic computing.
Memtransistors in which the source−drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic computing. On the other side, it is known that the complementary metal-oxide-semiconductor (CMOS) field effect transistors have played the fundamental role in the modern integrated circuit technology. Therefore, will complementary memtransistors (CMT) also play such a role in the future neuromorphic circuits and chips? In this review, various types of materials and physical mechanisms for constructing CMT (how) are inspected with their merits and need-to-address challenges discussed. Then the unique properties (what) and potential applications of CMT in different learning algorithms/scenarios of spiking neural networks (why) are reviewed, including supervised rule, reinforcement one, dynamic vision with in-sensor computing, etc. Through exploiting the complementary structure-related novel functions, significant reduction of hardware consuming, enhancement of energy/efficiency ratio and other advantages have been gained, illustrating the alluring prospect of design technology co-optimization (DTCO) of CMT towards neuromorphic computing.
AbstractFull TextPDF
The flexible perovskite light-emitting diodes (FPeLEDs), which can be expediently integrated to portable and wearable devices, have shown great potential in various applications. The FPeLEDs inherit the unique optical properties of metal halide perovskites, such as tunable bandgap, narrow emission linewidth, high photoluminescence quantum yield, and particularly, the soft nature of lattice. At present, substantial efforts have been made for FPeLEDs with encouraging external quantum efficiency (EQE) of 24.5%. Herein, we summarize the recent progress in FPeLEDs, focusing on the strategy developed for perovskite emission layers and flexible electrodes to facilitate the optoelectrical and mechanical performance. In addition, we present relevant applications of FPeLEDs in displays and beyond. Finally, perspective toward the future development and applications of flexible PeLEDs are also discussed.
The flexible perovskite light-emitting diodes (FPeLEDs), which can be expediently integrated to portable and wearable devices, have shown great potential in various applications. The FPeLEDs inherit the unique optical properties of metal halide perovskites, such as tunable bandgap, narrow emission linewidth, high photoluminescence quantum yield, and particularly, the soft nature of lattice. At present, substantial efforts have been made for FPeLEDs with encouraging external quantum efficiency (EQE) of 24.5%. Herein, we summarize the recent progress in FPeLEDs, focusing on the strategy developed for perovskite emission layers and flexible electrodes to facilitate the optoelectrical and mechanical performance. In addition, we present relevant applications of FPeLEDs in displays and beyond. Finally, perspective toward the future development and applications of flexible PeLEDs are also discussed.
AbstractFull TextPDF
Two-dimensional (2D) materials have attracted tremendous interest in view of the outstanding optoelectronic properties, showing new possibilities for future photovoltaic devices toward high performance, high specific power and flexibility. In recent years, substantial works have focused on 2D photovoltaic devices, and great progress has been achieved. Here, we present the review of recent advances in 2D photovoltaic devices, focusing on 2D-material-based Schottky junctions, homojunctions, 2D−2D heterojunctions, 2D−3D heterojunctions, and bulk photovoltaic effect devices. Furthermore, advanced strategies for improving the photovoltaic performances are demonstrated in detail. Finally, conclusions and outlooks are delivered, providing a guideline for the further development of 2D photovoltaic devices.
Two-dimensional (2D) materials have attracted tremendous interest in view of the outstanding optoelectronic properties, showing new possibilities for future photovoltaic devices toward high performance, high specific power and flexibility. In recent years, substantial works have focused on 2D photovoltaic devices, and great progress has been achieved. Here, we present the review of recent advances in 2D photovoltaic devices, focusing on 2D-material-based Schottky junctions, homojunctions, 2D−2D heterojunctions, 2D−3D heterojunctions, and bulk photovoltaic effect devices. Furthermore, advanced strategies for improving the photovoltaic performances are demonstrated in detail. Finally, conclusions and outlooks are delivered, providing a guideline for the further development of 2D photovoltaic devices.