Review Articles MORE

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  Topological materials-based photodetectors from the infrared to terahertz range

  Abstract Full Text PDF Infrared and terahertz waves constitute pivotal bands within the electromagnetic spectrum, distinguished by their robust penetration capabilities and non-ionizing nature. These wavebands offer the potential for achieving high-resolution and non-destructive detection methodologies, thereby possessing considerable research significance across diverse domains including communication technologies, biomedical applications, and security screening systems. Two-dimensional materials, owing to their distinctive optoelectronic attributes, have found widespread application in photodetection endeavors. Nonetheless, their efficacy diminishes when tasked with detecting lower photon energies. Furthermore, as the landscape of device integration evolves, two-dimensional materials struggle to align with the stringent demands for device superior performance. Topological materials, with their topologically protected electronic states and non-trivial topological invariants, exhibit quantum anomalous Hall effects and ultra-high carrier mobility, providing a new approach for seeking photosensitive materials for infrared and terahertz photodetectors. This article introduces various types of topological materials and their properties, followed by an explanation of the detection mechanism and performance parameters of photodetectors. Finally, it summarizes the current research status of near-infrared to far-infrared photodetectors and terahertz photodetectors based on topological materials, discussing the challenges faced and future prospects in their development.

  Infrared and terahertz waves constitute pivotal bands within the electromagnetic spectrum, distinguished by their robust penetration capabilities and non-ionizing nature. These wavebands offer the potential for achieving high-resolution and non-destructive detection methodologies, thereby possessing considerable research significance across diverse domains including communication technologies, biomedical applications, and security screening systems. Two-dimensional materials, owing to their distinctive optoelectronic attributes, have found widespread application in photodetection endeavors. Nonetheless, their efficacy diminishes when tasked with detecting lower photon energies. Furthermore, as the landscape of device integration evolves, two-dimensional materials struggle to align with the stringent demands for device superior performance. Topological materials, with their topologically protected electronic states and non-trivial topological invariants, exhibit quantum anomalous Hall effects and ultra-high carrier mobility, providing a new approach for seeking photosensitive materials for infrared and terahertz photodetectors. This article introduces various types of topological materials and their properties, followed by an explanation of the detection mechanism and performance parameters of photodetectors. Finally, it summarizes the current research status of near-infrared to far-infrared photodetectors and terahertz photodetectors based on topological materials, discussing the challenges faced and future prospects in their development.
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  Review on three-dimensional graphene: synthesis and joint photoelectric regulation in photodetectors

  Abstract Full Text PDF Graphene has garnered significant attention in photodetection due to its exceptional optical, electrical, mechanical, and thermal properties. However, the practical application of two-dimensional (2D) graphene in optoelectronic fields is limited by its weak light absorption (only 2.3%) and zero bandgap characteristics. Increasing light absorption is a critical scientific challenge for developing high-performance graphene-based photodetectors. Three-dimensional (3D) graphene comprises vertically grown stacked 2D-graphene layers and features a distinctive porous structure. Unlike 2D-graphene, 3D-graphene offers a larger specific surface area, improved electrochemical activity, and high chemical stability, making it a promising material for optoelectronic detection. Importantly, 3D-graphene has an optical microcavity structure that enhances light absorption through interaction with incoming light. This paper systematically reviews and analyzes the current research status and challenges of 3D-graphene-based photodetectors, aiming to explore feasible development paths for these devices and promote their industrial application.

  Graphene has garnered significant attention in photodetection due to its exceptional optical, electrical, mechanical, and thermal properties. However, the practical application of two-dimensional (2D) graphene in optoelectronic fields is limited by its weak light absorption (only 2.3%) and zero bandgap characteristics. Increasing light absorption is a critical scientific challenge for developing high-performance graphene-based photodetectors. Three-dimensional (3D) graphene comprises vertically grown stacked 2D-graphene layers and features a distinctive porous structure. Unlike 2D-graphene, 3D-graphene offers a larger specific surface area, improved electrochemical activity, and high chemical stability, making it a promising material for optoelectronic detection. Importantly, 3D-graphene has an optical microcavity structure that enhances light absorption through interaction with incoming light. This paper systematically reviews and analyzes the current research status and challenges of 3D-graphene-based photodetectors, aiming to explore feasible development paths for these devices and promote their industrial application.
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  Reconfigurable devices based on two-dimensional materials for logic and analog applications

  Abstract Full Text PDF In recent years, as the dimensions of the conventional semiconductor technology is approaching the physical limits, while the multifunction circuits are restricted by the relatively fixed characteristics of the traditional metal−oxide−semiconductor field-effect transistors, reconfigurable devices that can realize reconfigurable characteristics and multiple functions at device level have been seen as a promising method to improve integration density and reduce power consumption. Owing to the ultrathin structure, effective control of the electronic characteristics and ability to modulate structural defects, two-dimensional (2D) materials have been widely used to fabricate reconfigurable devices. In this review, we summarize the working principles and related logic applications of reconfigurable devices based on 2D materials, including generating tunable anti-ambipolar responses and demonstrating nonvolatile operations. Furthermore, we discuss the analog signal processing applications of anti-ambipolar transistors and the artificial intelligence hardware implementations based on reconfigurable transistors and memristors, respectively, therefore highlighting the outstanding advantages of reconfigurable devices in footprint, energy consumption and performance. Finally, we discuss the challenges of the 2D materials-based reconfigurable devices.

  In recent years, as the dimensions of the conventional semiconductor technology is approaching the physical limits, while the multifunction circuits are restricted by the relatively fixed characteristics of the traditional metal−oxide−semiconductor field-effect transistors, reconfigurable devices that can realize reconfigurable characteristics and multiple functions at device level have been seen as a promising method to improve integration density and reduce power consumption. Owing to the ultrathin structure, effective control of the electronic characteristics and ability to modulate structural defects, two-dimensional (2D) materials have been widely used to fabricate reconfigurable devices. In this review, we summarize the working principles and related logic applications of reconfigurable devices based on 2D materials, including generating tunable anti-ambipolar responses and demonstrating nonvolatile operations. Furthermore, we discuss the analog signal processing applications of anti-ambipolar transistors and the artificial intelligence hardware implementations based on reconfigurable transistors and memristors, respectively, therefore highlighting the outstanding advantages of reconfigurable devices in footprint, energy consumption and performance. Finally, we discuss the challenges of the 2D materials-based reconfigurable devices.
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  Revolutionizing neuromorphic computing with memristor-based artificial neurons

  Abstract Full Text PDF As traditional von Neumann architectures face limitations in handling the demands of big data and complex computational tasks, neuromorphic computing has emerged as a promising alternative, inspired by the human brain's neural networks. Volatile memristors, particularly Mott and diffusive memristors, have garnered significant attention for their ability to emulate neuronal dynamics, such as spiking and firing patterns, enabling the development of reconfigurable and adaptive computing systems. Recent advancements include the implementation of leaky integrate-and-fire neurons, Hodgkin−Huxley neurons, optoelectronic neurons, and time-surface neurons, all utilizing volatile memristors to achieve efficient, low-power, and highly integrated neuromorphic systems. This paper reviews the latest progress in volatile memristor-based artificial neurons, highlighting their potential for energy-efficient computing and integration with artificial synapses. We conclude by addressing challenges such as improving memristor reliability and exploring new architectures to advance memristor-based neuromorphic computing.

  As traditional von Neumann architectures face limitations in handling the demands of big data and complex computational tasks, neuromorphic computing has emerged as a promising alternative, inspired by the human brain's neural networks. Volatile memristors, particularly Mott and diffusive memristors, have garnered significant attention for their ability to emulate neuronal dynamics, such as spiking and firing patterns, enabling the development of reconfigurable and adaptive computing systems. Recent advancements include the implementation of leaky integrate-and-fire neurons, Hodgkin−Huxley neurons, optoelectronic neurons, and time-surface neurons, all utilizing volatile memristors to achieve efficient, low-power, and highly integrated neuromorphic systems. This paper reviews the latest progress in volatile memristor-based artificial neurons, highlighting their potential for energy-efficient computing and integration with artificial synapses. We conclude by addressing challenges such as improving memristor reliability and exploring new architectures to advance memristor-based neuromorphic computing.