Miniaturized spectrometers in the mid-infrared (MIR) are critical in developing next-generation portable electronics for advanced sensing and analysis. The bulky gratings or detector/filter arrays in conventional micro-spectrometers set a physical limitation to their miniaturization. In this work, we demonstrate a single-pixel MIR micro-spectrometer that reconstructs the sample transmission spectrum by a spectrally dispersed light source instead of spatially grated light beams. The spectrally tunable MIR light source is realized based on the thermal emissivity engineered via the metal-insulator phase transition of vanadium dioxide (VO2). We validate the performance by showing that the transmission spectrum of a magnesium fluoride (MgF2) sample can be computationally reconstructed from sensor responses at varied light source temperatures. With potentially minimum footprint due to the array-free design, our work opens the possibility where compact MIR spectrometers are integrated into portable electronic systems for versatile applications.

With the developing technologies of artificial intelligence and the Internet of Things, intelligent IoT (iIoT) is prevailing currently. Design and implementation of integrated IoT nodes with continuous perception capability are indispensable to realize various smart terminal devices, which would also be vital to reduce the power consumption, improve the real-time performance, and enhance the security/privacy of the IoT system. In this paper, we present the architecture of “Sensing with Computing” and its chip design for smart sensing applications, which would support multi-modal perception signal processing with multi-dimension extension ability. Specially, we explore the analog/mixed-signal circuit designs and algorithm-hardware co-design methodologies for perception signal processing, and we also study the multi-modal integration of novel sensors and their interface technologies. Additionally, some multi-modal smart sensing systems with “Sensing + Computing in Memory” mixed-signal chips would be fabricated, which would support typical always-on smart sensing tasks.

Ferroelectric diodes can generate a polarization-controlled bidirectional photoresponse to simulate inhibition and promotion behaviors in the artificial neuromorphic system with fast speed, high energy efficiency, and nonvolatility. However, the existing ferroelectric diodes based on ferroelectric oxides suffer from a weak bidirectional photoresponse (below 1 mA/W), difficult miniaturization, and a large response photon energy (over 3 eV). Here, we design a series of van der Waals �−In2Se3/Nb�2 (� = S, Se, and Te) ferroelectric diodes with bidirectional photoresponse by using ab initio quantum transport simulation. These devices show a maximum bidirectional photoresponse of 30 (−19) mA/W and a minimum response photon energy of 1.3 eV at the monolayer thickness. Our work shows advanced optoelectronic applications of the van der Waals ferroelectric diodes in the future artificial neuromorphic system.

The wake-up and fatigue effects exhibited by ferroelectric hafnium oxide (HfO2) during electrical cycling are two of the most significant obstacles limiting its development and application. Despite a mainstream theory relating these phenomena to the migration of oxygen vacancies and the evolution of the built-in field, no supportive experimental observations from a nanoscale perspective have been reported so far. By combining differential phase contrast scanning transmission electron microscopy (DPC-STEM) and energy dispersive spectroscopy (EDS) analysis, we directly observe the migration of oxygen vacancies and the evolution of the built-in field in ferroelectric HfO2 for the first time. These solid results indicate that the wake-up effect is caused by the homogenization of oxygen vacancy distribution and weakening of the vertical built-in field whereas the fatigue effect is related to charge injection and transverse local electric field enhancement. In addition, using a low-amplitude electrical cycling scheme, we exclude field-induced phase transition from the root cause of the wake-up and fatigue in Hf0.5Zr0.5O2. With direct experimental evidence, this work clarifies the core mechanism of the wake-up and fatigue effects, which is important for the optimization of ferroelectric memory devices.

Adaptive radiative cooling offers smart thermal regulation that saves energy for conditioning regardless of the variation in environment or requirements. In a recent study by Banerjee and colleagues, an electrochromic device based on the redox process of PEDOT was developed, enabling tunable surface temperature by applied electrical bias at ambient conditions.

Abstract Complementary metal-oxide-semiconductor (CMOS) field-effect transistors (FETs) are the key component of a chip. Bulk indium arsenide (InAs) owns nearly 30 times higher electron mobility µe than silicon but suffers from a much lower hole mobility µh (µe/µh = 80), thus unsuited to CMOS application with a single material. Through the accurate ab initio quantum-transport simulations, the performance gap between the NMOS and PMOS is significantly narrowed is predicted and even vanished in the sub-2-nm-diameter gate-all-around (GAA) InAs nanowires (NW) FETs because the inversion of the light and heavy hole bands occurs when the diameter is shorter than 3 nm. It is further proposed several feasible strategies for further improving the performance symmetry in the GAA InAs NWFETs. Short-channel effects are effectively depressed in the symmetric n- and p-type GAA InAs NWFETs till the gate length is scaled down to 2 nm according to the standards of the International Technology Roadmap for Semiconductors. Therefore, the ultrasmall GAA InAs NWFETs possess tremendous prospects in CMOS integrated circuits.

In face of the critical endurance issue, for the first time we take a holistic perspective to co-optimize the ferroelectric materials and interlayer in FeFET. Compared to the common HZO based gate stack, the novel combination of Hf0.95 Al0.05 O2+Al2 O3 enhances the endurance to $\gt 5 \times 10 ^9$ cycles while maintaining a retention > 10 years. In-depth analysis based on DFT and DQSCV reveal the reduction of interlayer electric field and interface charge trapping as the mechanism of optimization. We also develop a distributed interface trap model to correlate different trapping dynamics with the interlayer property in each device. This work pushes forward the understanding and development of high endurance strategy for FeFET.

Phase transitions can occur in certain materials such as transition metal oxides (TMOs) and chalcogenides when there is a change in external conditions such as temperature and pressure. Along with phase transitions in these phase change materials (PCMs) come dramatic contrasts in various physical properties, which can be engineered to manipulate electrons, photons, polaritons, and phonons at the nanoscale, offering new opportunities for reconfigurable, active nanodevices. In this review, we particularly discuss phase-transition-enabled active nanotechnologies in nonvolatile electrical memory, tunable metamaterials, and metasurfaces for manipulation of both free-space photons and in-plane polaritons, and multifunctional emissivity control in the infrared (IR) spectrum. The fundamentals of PCMs are first introduced to explain the origins and principles of phase transitions. Thereafter, we discuss multiphysical nanodevices for electronic, photonic, and thermal management, attesting to the broad applications and exciting promises of PCMs. Emerging trends and valuable applications in all-optical neuromorphic devices, thermal data storage, and encryption are outlined in the end.

Energy-efficient components that are capable of intelligently regulating room temperature are much demanded to reduce the energy consumption in buildings. In recent years, phase change materials (PCMs) have been widely investigated for intelligent temperature regulation by taking advantages of their unique thermal, optical, and mechanical properties across phase transition. In this review, we summarize the mechanisms of PCMs for intelligent temperature regulation, including latent heat, optical modulation, and mechanical deformation. We then discuss the traditional PCMs, such as organic and inorganic PCMs with huge latent heats, and emerging PCMs, such as VO2, for the applications in temperature controls, smart windows, and radiative cooling surfaces. We finally point out where to focus for these PCMs to realize applications in buildings. This review provides insights into future research of PCMs for their intelligent applications.

Electrical synapses provide rapid, bidirectional communication in nervous systems, accomplishing tasks distinct from and complementary to chemical synapses. Here, we demonstrate an artificial electrical synapse based on second-order conductance transition (SOCT) in an Ag-based memristor for the first time. High-resolution transmission electron microscopy indicates that SOCT is mediated by the virtual silver electrode. Besides the conventional chemical synaptic behaviors, the biphasic plasticity of electrical synapses is well emulated by integrating the device with a photosensitive element to form an optical pre-processing unit (OPU), which contributes to the retinal neural circuitry and is adaptive to ambient illumination. By synergizing the OPU and spiking neural network (SNN), adaptive pattern recognition tasks are accomplished under different light and noise settings. This work not only contributes to the further completion of synaptic behaviour for hardware-level neuromorphic computing, but also potentially enables image pre-processing with light adaptation and noise suppression for adaptive visual recognition.

Abstract Recent studies indicate that synaptic scaling is a vital mechanism to solve instability risks brought by the positive feedback of synaptic weight change related with standalone Hebbian plasticity. There are two kinds of synaptic scaling in the neural network, including local scaling and global scaling, both important for stabilizing the neural function. In this paper, for the first time, local synaptic scaling is emulated based on the MoS2 neuristor. The first-principle calculation reveals that synaptic scaling achieved by the neuristor is associated with an internal residual Li+-related weak dynamical process. Experimental results show the potential of achieving global synaptic scaling by the same device. Moreover, inspired by the synaptic scaling in the human brain, a new method of weight mapping called weight scaling mapping (WSM) is proposed to improve the stability of an artificial neural network (ANN). The simulation results indicate that WSM can improve the accuracy and anti-noise ability of the network compared with the traditional mapping method. These findings provide new insight into bionic research and help advance the construction of stable neuromorphic systems.