A negative capacitance (NC) effect has been proposed as a critical pathway to overcome the 'Boltzmann tyranny' of electrons, achieve the steep slope operation of transistors and reduce the power dissipation of current semiconductor devices. In particular, the ferroic property in hafnium-based films with fluorite structure provides an opportunity for the application of the NC effect in electronic devices. However, to date, only a transient NC effect has been confirmed in hafnium-based ferroic materials, which is usually accompanied by hysteresis and is detrimental to low-power transistor operations. The stabilized NC effect enables hysteresis-free and low-power transistors but is difficult to observe and demonstrate in hafnium-based films. This difficulty is closely related to the polycrystalline and multi-phase structure of hafnium-based films fabricated by atomic layer deposition or chemical solution deposition. Here, we prepare epitaxial ferroelectric Hf_0.5Zr_0.5O₂ and antiferroelectric ZrO₂ films with single-phase structure and observe the capacitance enhancement effect of Hf_0.5Zr_0.5O₂/Al₂O₃ and ZrO₂/Al₂O₃ capacitors compared to that of the isolated Al₂O₃ capacitor, verifying the stabilized NC effect. The capacitance of Hf_0.5Zr_0.5O₂ and ZrO₂ is evaluated as −17.41 and −27.64 pF, respectively. The observation of the stabilized NC effect in hafnium-based films sheds light on NC studies and paves the way for low-power transistors.

The integration of sensory information from different modalities, such as touch and vision, is essential for organisms to perform behavioral functions such as decision-making, learning, and memory. Artificial implementation of human multi-sensory perception using electronic supports is of great significance for achieving efficient human–machine interaction. Thanks to their structural and functional similarity with biological synapses, memristors are emerging as promising nanodevices for developing artificial neuromorphic perception. Memristive devices can sense multidimensional signals including light, pressure, and sound. Their in-sensor computing architecture represents an ideal platform for efficient multimodal perception. We review recent progress in multimodal memristive technology and its application to neuromorphic perception of complex stimuli carrying visual, olfactory, auditory, and tactile information. At the device level, the operation model and undergoing mechanism have also been introduced. Finally, we discuss the challenges and prospects associated with this rapidly progressing field of research.

Recently, off-centering behavior has been discovered in a series of thermoelectric materials. This behavior indicates that the constituent atoms of the lattice displace from their coordination centers, leading to the locally distorted state and local symmetry breaking, while the material still retains its original crystallographic symmetry. This effect has been proved to be the root cause of ultralow thermal conductivity in off-centering materials, and is considered as an effective tool to regulate the thermal conductivity and improve the thermoelectric performance. Herein, we present a collection of recently discovered off-centering compounds, discuss their electronic origins and local coordination structures, and illuminate the underlying mechanism of the off-centering effect on phonon transport and thermal conductivity. This paper presents a comprehensive view of our current understanding to the off-centering effect, and provides a new idea for designing high performance thermoelectrics.

Amorphous Ga₂O₃ (a-Ga₂O₃) has been attracting more and more attention due to its unique merits such as wide bandgap (∼4.9 eV), low growth temperature, large-scale uniformity, low cost and energy efficient, making it a powerful competitor in flexible deep ultraviolet (UV) photodetection. Although the responsivity of the ever-reported a-Ga₂O₃ UV photodetectors (PDs) is usually in the level of hundreds of A/W, it is often accompanied by a large dark current due to the presence of abundant oxygen vacancy (V_O) defects, which severely limits the possibility to detect weak signals and achieve versatile applications. In this work, the V_O defects in a-Ga₂O₃ thin films are successfully passivated by in-situ hydrogen doping during the magnetron sputtering process. As a result, the dark current of a-Ga₂O₃ UV PD is remarkably suppressed to 5.17 × 10⁻¹¹ A at a bias of 5 V. Importantly, the photocurrent of the corresponding device is still as high as 1.37 × 10⁻³ A, leading to a high photo-to-dark current ratio of 2.65 × 10⁷ and the capability to detect the UV light with the intensity below 10 nW cm⁻². Moreover, the H-doped a-Ga₂O₃ thin films have also been deposited on polyethylene naphtholate substrates to construct flexible UV PDs, which exhibit no great degradation in bending states and fatigue tests. These results demonstrate that hydrogen doping can effectively improve the performance of a-Ga₂O₃ UV PDs, further promoting its practical application in various areas.

Neuromorphic systems represent a promising avenue for the development of the next generation of artificial intelligence hardware. Machine vision, one of the cores in artificial intelligence, requires system-level support with low power consumption, low latency, and parallel computing. Neuromorphic vision sensors provide an efficient solution for machine vision by simulating the structure and function of the biological retina. Optoelectronic synapses, which use light as the main means to achieve the dual functions of photosensitivity and synapse, are the basic units of the neuromorphic vision sensor. Therefore, it is necessary to develop various optoelectronic synaptic devices to expand the application scenarios of neuromorphic vision systems. This review compares the structure and function for both biological and artificial retina systems, and introduces various optoelectronic synaptic devices based on low-dimensional materials and working mechanisms. In addition, advanced applications of optoelectronic synapses as neuromorphic vision sensors are comprehensively summarized. Finally, the challenges and prospects in this field are briefly discussed.

Artificial intelligence has become indispensable in modern life, but its energy consumption has become a significant concern due to its huge storage and computational demands. Artificial intelligence algorithms are mainly based on deep learning algorithms, relying on the backpropagation of convolutional neural networks or binary neural networks. While these algorithms aim to simulate the learning process of the human brain, their low bio-fidelity and the separation of storage and computing units lead to significant energy consumption. The human brain is a remarkable computing machine with extraordinary capabilities for recognizing and processing complex information while consuming very low power. Tunneling magnetoresistance (TMR)-based devices, namely magnetic tunnel junctions (MTJs), have great advantages in simulating the behavior of biological synapses and neurons. This is not only because MTJs can simulate biological behavior such as spike-timing dependence plasticity and leaky integrate-fire, but also because MTJs have intrinsic stochastic and oscillatory properties. These characteristics improve MTJs' bio-fidelity and reduce their power consumption. MTJs also possess advantages such as ultrafast dynamics and non-volatile properties, making them widely utilized in the field of neuromorphic computing in recent years. We conducted a comprehensive review of the development history and underlying principles of TMR, including a detailed introduction to the material and magnetic properties of MTJs and their temperature dependence. We also explored various writing methods of MTJs and their potential applications. Furthermore, we provided a thorough analysis of the characteristics and potential applications of different types of MTJs for neuromorphic computing. TMR-based devices have demonstrated promising potential for broad application in neuromorphic computing, particularly in the development of spiking neural networks. Their ability to perform on-chip learning with ultra-low power consumption makes them an exciting prospect for future advances in the era of the internet of things.

It is crucial to develop an advanced artificially intelligent optoelectronic information system that accurately simulates photonic nociceptors like the activation process of a human visual nociceptive pathway. Visible light reaches the retina for human visual perception, but its excessive exposure can damage nearby tissues. However, there are relatively few reports on visible light–triggered nociceptors. Here, we introduce a two-dimensional natural defective III–VI semiconductor β-In₂S₃ and utilize its broad spectral response, including visible light brought by intrinsic defects, for visible light–triggered artificial photonic nociceptors. The response mode of the device, under visible light excitation, is very similar to that of the human eye. It perfectly reproduces the pain perception characteristics of the human visual system, such as 'threshold,' 'relaxation,' 'no adaptation', and 'sensitization'. Its working principle is attributed to the mechanism of charge trapping associated with the intrinsic vacancies in In2S3 nanosheets. This work provides an attractive material system (intrinsic defective semiconductors) for broadband artificial photonic nociceptors.

Artificial synapses are electronic devices that simulate important functions of biological synapses, and therefore are the basic components of artificial neural morphological networks for brain-like computing. One of the most important objectives for developing artificial synapses is to simulate the characteristics of biological synapses as much as possible, especially their self-adaptive ability to external stimuli. Here, we have successfully developed an artificial synapse with multiple synaptic functions and highly adaptive characteristics based on a simple SrTiO₃/Nb: SrTiO₃ heterojunction type memristor. Diverse functions of synaptic learning, such as short-term/long-term plasticity (STP/LTP), transition from STP to LTP, learning–forgetting–relearning behaviors, associative learning and dynamic filtering, are all bio-realistically implemented in a single device. The remarkable synaptic performance is attributed to the fascinating inherent dynamics of oxygen vacancy drift and diffusion, which give rise to the coexistence of volatile- and nonvolatile-type resistive switching. This work reports a multi-functional synaptic emulator with advanced computing capability based on a simple heterostructure, showing great application potential for a compact and low-power neuromorphic computing system.