Journal of Semiconductors

Since the invention of amorphous indium–gallium–zinc–oxide (IGZO) based thin-film transistors (TFTs) by Hideo Hosono in 2004, investigations on the topic of IGZO TFTs have been rapidly expanded thanks to their high electrical performance, large-area uniformity, and low processing temperature. This article reviews the recent progress and major trends in the field of IGZO-based TFTs. After a brief introduction of the history of IGZO and the main advantages of IGZO-based TFTs, an overview of IGZO materials and IGZO-based TFTs is given. In this part, IGZO material electron travelling orbitals and deposition methods are introduced, and the specific device structures and electrical performance are also presented. Afterwards, the recent advances of IGZO-based TFT applications are summarized, including flat panel display drivers, novel sensors, and emerging neuromorphic systems. In particular, the realization of flexible electronic systems is discussed. The last part of this review consists of the conclusions and gives an outlook over the field with a prediction for the future.

Current electronics are driven by advanced microfabrication for fast and efficient information processing. In spite of high performance, these wafer-based devices are rigid, non-degradable, and unable to autonomous repair. Skin-inspired electronics have emerged as a new class of devices and systems for next-generation flexible and wearable electronics. The technology gains inspiration from the structures, properties, and sensing mechanisms of the skin, which may find a broad range of applications in cutting-edge fields such as healthcare monitoring, human-machine interface, and soft robotics/prostheses. Practical demands have fueled the development of electronic materials with skin-like properties in terms of stretchability, self-healing capability, and biodegradability. These materials provide the basis for functional sensors with innovative and biomimetic designs. Further system-level integrations and optimizations enable new forms of electronics for real-world applications. This review summarizes recent advancements in this active area and speculates on future directions.

Solar water splitting is a promising strategy for sustainable production of renewable hydrogen, and solving the crisis of energy and environment in the world. However, large-scale application of this method is hampered by the efficiency and the expense of the solar water splitting systems. Searching for non-toxic, low-cost, efficient and stable photocatalysts is an important way for solar water splitting. Due to the simplicity of structure and the flexibility of composition, perovskite based photocatalysts have recently attracted widespread attention for application in solar water splitting. In this review, the recent developments of perovskite based photocatalysts for water splitting are summarized. An introduction including the structures and properties of perovskite materials, and the fundamentals of solar water splitting is first provided. Then, it specifically focuses on the strategies for designing and modulating perovskite materials to improve their photocatalytic performance for solar water splitting. The current challenges and perspectives of perovskite materials in solar water splitting are also reviewed. The aim of this review is to summarize recent findings and developments of perovskite based photocatalysts and provide some useful guidance for the future research on the design and development of highly efficient perovskite based photocatalysts and the relevant systems for water splitting.

Lightweight and flexible self-charging power systems with synchronous energy harvesting and energy storage abilities are highly desired in the era of the internet of things and artificial intelligences, which can provide stable, sustainable, and autonomous power sources for ubiquitous, distributed, and low-power wearable electronics. However, there is a lack of comprehensive review and challenging discussion on the state-of-the-art of the triboelectric nanogenetor (TENG)-based self-charging power textiles, which have a great possibility to become the future energy autonomy power sources. Herein, the recent progress of the self-charging power textiles hybridizing fiber/fabric based TENGs and fiber/fabric shaped batteries/supercapacitors is comprehensively summarized from the aspect of textile structural designs. Based on the current research status, the key bottlenecks and brighter prospects of self-charging power textiles are also discussed in the end. It is hoped that the summary and prospect of the latest research of self-charging power textiles can help relevant researchers accurately grasp the research progress, focus on the key scientific and technological issues, and promote further research and practical application process.

Flexible photodetectors have garnered significant attention by virtue of their potential applications in environmental monitoring, wearable healthcare, imaging sensing, and portable optical communications. Perovskites stand out as particularly promising materials for photodetectors, offering exceptional optoelectronic properties, tunable band gaps, low-temperature solution processing, and notable mechanical flexibility. In this review, we explore the latest progress in flexible perovskite photodetectors, emphasizing the strategies developed for photoactive materials and device structures to enhance optoelectronic performance and stability. Additionally, we discuss typical applications of these devices and offer insights into future directions and potential applications.

The rapid advancement of information technology has heightened interest in complementary devices and circuits. Conventional p-type semiconductors often lack sufficient electrical performance, thus prompting the search for new materials with high hole mobility and long-term stability. Elemental tellurium (Te), featuring a one-dimensional chiral atomic structure, has emerged as a promising candidate due to its narrow bandgap, high hole mobility, and versatility in industrial applications, particularly in electronics and renewable energy. This review highlights recent progress in Te nanostructures and related devices, focusing on synthesis methods, including vapor deposition and hydrothermal synthesis, which produce Te nanowires, nanorods, and other nanostructures. Critical applications in photodetectors, gas sensors, and energy harvesting devices are discussed, with a special emphasis on their role within the internet of things (IoT) framework, a rapidly growing field that is reshaping our technological landscape. The prospects and potential applications of Te-based technologies are also highlighted.

Organic electrochemical transistors have emerged as a solution for artificial synapses that mimic the neural functions of the brain structure, holding great potentials to break the bottleneck of von Neumann architectures. However, current artificial synapses rely primarily on electrical signals, and little attention has been paid to the vital role of neurotransmitter-mediated artificial synapses. Dopamine is a key neurotransmitter associated with emotion regulation and cognitive processes that needs to be monitored in real time to advance the development of disease diagnostics and neuroscience. To provide insights into the development of artificial synapses with neurotransmitter involvement, this review proposes three steps towards future biomimic and bioinspired neuromorphic systems. We first summarize OECT-based dopamine detection devices, and then review advances in neurotransmitter-mediated artificial synapses and resultant advanced neuromorphic systems. Finally, by exploring the challenges and opportunities related to such neuromorphic systems, we provide a perspective on the future development of biomimetic and bioinspired neuromorphic systems.

The heterogeneous integration of III–V devices with Si-CMOS on a common Si platform has shown great promise in the new generations of electrical and optical systems for novel applications, such as HEMT or LED with integrated control circuitry. For heterogeneous integration, direct wafer bonding (DWB) techniques can overcome the materials and thermal mismatch issues by directly bonding dissimilar materials systems and device structures together. In addition, DWB can perform at wafer-level, which eases the requirements for integration alignment and increases the scalability for volume production. In this paper, a brief review of the different bonding technologies is discussed. After that, three main DWB techniques of single-, double- and multi-bonding are presented with the demonstrations of various heterogeneous integration applications. Meanwhile, the integration challenges, such as micro-defects, surface roughness and bonding yield are discussed in detail.

Synaptic nano-devices have powerful capabilities in logic, memory and learning, making them essential components for constructing brain-like neuromorphic computing systems. Here, we have successfully developed and demonstrated a synaptic nano-device based on Ga₂O₃ nanowires with low energy consumption. Under 255 nm light stimulation, the biomimetic synaptic nano-device can stimulate various functionalities of biological synapses, including pulse facilitation, peak time-dependent plasticity and memory learning ability. It is found that the artificial synaptic device based on Ga₂O₃ nanowires can achieve an excellent "learning−forgetting−relearning" functionality. The transition from short-term memory to long-term memory and retention of the memory level after the stepwise learning can attribute to the great relearning functionality of Ga₂O₃ nanowires. Furthermore, the energy consumption of the synaptic nano-device can be lower than 2.39 × 10^‒11 J for a synaptic event. Moreover, our device demonstrates exceptional stability in long-term stimulation and storage. In the application of neural morphological computation, the accuracy of digit recognition exceeds 90% after 12 training sessions, indicating the strong learning capability of the cognitive system composed of this synaptic nano-device. Therefore, our work paves an effective way for advancing hardware-based neural morphological computation and artificial intelligence systems requiring low power consumption.

Memristors have a synapse-like two-terminal structure and electrical properties, which are widely used in the construction of artificial synapses. However, compared to inorganic materials, organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance. Here, for the first time, we present an organic memristor based on an electropolymerized dopamine-based memristive layer. This polydopamine-based memristor demonstrates the improvements in key performance, including a low threshold voltage of 0.3 V, a thin thickness of 16 nm, and a high parasitic capacitance of about 1 μFmm⁻². By leveraging these properties in combination with its stable threshold switching behavior, we construct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage, whose spiking frequency increases with the increase of current stimulation analogous to a biological neuron. The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.

The hierarchical and coordinated processing of visual information by the brain demonstrates its superior ability to minimize energy consumption and maximize signal transmission efficiency. Therefore, it is crucial to develop artificial visual synapses that integrate optical sensing and synaptic functions. This study fully leverages the excellent photoresponsivity properties of the PM6 : Y6 system to construct a vertical photo-tunable organic memristor and conducts in-depth research on its resistive switching performance, photodetection capability, and simulation of photo-synaptic behavior, showcasing its excellent performance in processing visual information and simulating neuromorphic behaviors. The device achieves stable and gradual resistance change, successfully simulating voltage-controlled long-term potentiation/depression (LTP/LTD), and exhibits various photo-electric synergistic regulation of synaptic plasticity. Moreover, the device has successfully simulated the image perception and recognition functions of the human visual nervous system. The non-volatile Au/PM6 : Y6/ITO memristor is used as an artificial synapse and neuron modeling, building a hierarchical coordinated processing SLP-CNN cascade neural network for visual image recognition training, its linear tunable photoconductivity characteristic serves as the weight update of the network, achieving a recognition accuracy of up to 93.4%. Compared with the single-layer visual target recognition model, this scheme has improved the recognition accuracy by 19.2%.

In recent years, research focusing on synaptic device based on phototransistors has provided a new method for associative learning and neuromorphic computing. A TiO₂/AlGaN/GaN heterostructure-based synaptic phototransistor is fabricated and measured, integrating a TiO₂ nanolayer gate and a two-dimensional electron gas (2DEG) channel to mimic the synaptic weight and the synaptic cleft, respectively. The maximum drain to source current is 10 nA, while the device is driven at a reverse bias not exceeding −2.5 V. A excitatory postsynaptic current (EPSC) of 200 nA can be triggered by a 365 nm UVA light spike with the duration of 1 s at light intensity of 1.35 μWcm⁻². Multiple synaptic neuromorphic functions, including EPSC, short-term/long-term plasticity (STP/LTP) and paried-pulse facilitation (PPF), are effectively mimicked by our GaN-based heterostructure synaptic device. In the typical Pavlov's dog experiment, we demonstrate that the device can achieve "retraining" process to extend memory time through enhancing the intensity of synaptic weight, which is similar to the working mechanism of human brain.

The traditional von Neumann architecture has demonstrated inefficiencies in parallel computing and adaptive learning, rendering it incapable of meeting the growing demand for efficient and high-speed computing. Neuromorphic computing with significant advantages such as high parallelism and ultra-low power consumption is regarded as a promising pathway to overcome the limitations of conventional computers and achieve the next-generation artificial intelligence. Among various neuromorphic devices, the artificial synapses based on electrolyte-gated transistors stand out due to their low energy consumption, multimodal sensing/recording capabilities, and multifunctional integration. Moreover, the emerging optoelectronic neuromorphic devices which combine the strengths of photonics and electronics have demonstrated substantial potential in the neuromorphic computing field. Therefore, this article reviews recent advancements in electrolyte-gated optoelectronic neuromorphic transistors. First, it provides an overview of artificial optoelectronic synapses and neurons, discussing aspects such as device structures, operating mechanisms, and neuromorphic functionalities. Next, the potential applications of optoelectronic synapses in different areas such as artificial visual system, pain system, and tactile perception systems are elaborated. Finally, the current challenges are summarized, and future directions for their developments are proposed.

Flexible photodetectors have garnered significant attention by virtue of their potential applications in environmental monitoring, wearable healthcare, imaging sensing, and portable optical communications. Perovskites stand out as particularly promising materials for photodetectors, offering exceptional optoelectronic properties, tunable band gaps, low-temperature solution processing, and notable mechanical flexibility. In this review, we explore the latest progress in flexible perovskite photodetectors, emphasizing the strategies developed for photoactive materials and device structures to enhance optoelectronic performance and stability. Additionally, we discuss typical applications of these devices and offer insights into future directions and potential applications.

Heart rate variability (HRV) that can reflect the dynamic balance between the sympathetic nervous and parasympathetic nervous of human autonomic nervous system (ANS) has attracted considerable attention. However, traditional electrocardiogram (ECG) devices for HRV analysis are bulky, and hard wires are needed to attach measuring electrodes to the chest, resulting in the poor wearable experience during the long-term measurement. Compared with that, wearable electronics enabling continuously cardiac signals monitoring and HRV assessment provide a desirable and promising approach for helping subjects determine sleeping issues, cardiovascular diseases, or other threats to physical and mental well-being. Until now, significant progress and advances have been achieved in wearable electronics for HRV monitoring and applications for predicting human physical and mental well-being. In this review, the latest progress in the integration of wearable electronics and HRV analysis as well as practical applications in assessment of human physical and mental health are included. The commonly used methods and physiological signals for HRV analysis are briefly summarized. Furthermore, we highlighted the research on wearable electronics concerning HRV assessment and diverse applications such as stress estimation, drowsiness detection, etc. Lastly, the current limitations of the integrated wearable HRV system are concluded, and possible solutions in such a research direction are outlined.

With the rapid development of artificial intelligence (AI) technology, the demand for high-performance and energy-efficient computing is increasingly growing. The limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a solution. Neuromorphic computing mimics the working principles of the human brain, characterized by high efficiency, low energy consumption, and strong fault tolerance, providing a hardware foundation for the development of new generation AI technology. Artificial neurons and synapses are the two core components of neuromorphic computing systems. Artificial perception is a crucial aspect of neuromorphic computing, where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of research. This work reviews recent advances in artificial sensory neurons and their applications. First, biological sensory neurons are briefly described. Then, different types of artificial neurons, such as transistor neurons and memristive neurons, are discussed in detail, focusing on their device structures and working mechanisms. Next, the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated, covering various sensory types, including vision, touch, hearing, taste, and smell. Finally, challenges faced by artificial sensory neurons at both device and system levels are summarized.

Infrared optoelectronic sensing is the core of many critical applications such as night vision, health and medication, military, space exploration, etc. Further including mechanical flexibility as a new dimension enables novel features of adaptability and conformability, promising for developing next-generation optoelectronic sensory applications toward reduced size, weight, price, power consumption, and enhanced performance (SWaP³). However, in this emerging research frontier, challenges persist in simultaneously achieving high infrared response and good mechanical deformability in devices and integrated systems. Therefore, we perform a comprehensive review of the design strategies and insights of flexible infrared optoelectronic sensors, including the fundamentals of infrared photodetectors, selection of materials and device architectures, fabrication techniques and design strategies, and the discussion of architectural and functional integration towards applications in wearable optoelectronics and advanced image sensing. Finally, this article offers insights into future directions to practically realize the ultra-high performance and smart sensors enabled by infrared-sensitive materials, covering challenges in materials development and device micro-/nanofabrication. Benchmarks for scaling these techniques across fabrication, performance, and integration are presented, alongside perspectives on potential applications in medication and health, biomimetic vision, and neuromorphic sensory systems, etc.

Organic electrochemical transistors have emerged as a solution for artificial synapses that mimic the neural functions of the brain structure, holding great potentials to break the bottleneck of von Neumann architectures. However, current artificial synapses rely primarily on electrical signals, and little attention has been paid to the vital role of neurotransmitter-mediated artificial synapses. Dopamine is a key neurotransmitter associated with emotion regulation and cognitive processes that needs to be monitored in real time to advance the development of disease diagnostics and neuroscience. To provide insights into the development of artificial synapses with neurotransmitter involvement, this review proposes three steps towards future biomimic and bioinspired neuromorphic systems. We first summarize OECT-based dopamine detection devices, and then review advances in neurotransmitter-mediated artificial synapses and resultant advanced neuromorphic systems. Finally, by exploring the challenges and opportunities related to such neuromorphic systems, we provide a perspective on the future development of biomimetic and bioinspired neuromorphic systems.