We report large Lande g-factors observed in PbTe-Pb hybrid nanowires. The g-factor can reach 83, significantly larger than those in bare PbTe nanowires (typically below 20). We attribute this enhancement to orbital effects in the superconducting film, particularly when the magnetic field is nearly perpendicular to the Pb film. This enhancement is beneficial for the search for topological superconductivity by reducing the critical magnetic field required for the phase transition.

Shifting the computing unit from back-end electronic processors to sensors or front-end free-space optics has emerged as a promising solution for enhancing the energy efficiency of visual processing. However, existing optoelectronic computing arrays often struggle to achieve a rich diversity of optoelectronic responses at low hardware costs, failing to balance array integration scale with functional diversity. Here we develop a multi-responsive retinomorphic sensor for reconfigurable optoelectronic computing. Under low-bias voltage control, the two-terminal Sb2Te3/MoS2 heterostructure enables in situ, reversible switching between photodiode and opto-synaptic responses. Meanwhile, it can mimick the intrinsic leaky integrate-and-fire behaviour of biological neurons under focused light pulses. Furthermore, by integrating a sensor array of a specific scale with diffractive optical components, we showcase multi-mode optoelectronic computing capabilities, such as image and video processing, as well as transfer learning applications enabled by optical spike encoding. Leveraging the multiple optoelectronic responses of the sensor, the architecture achieves a superior spatiotemporal dimensionality that is promising for applications in fields such as autonomous driving, satellite remote sensing and robotics. A multi-responsive retinomorphic sensor based on a two-terminal Sb2Te3/MoS2 heterostructure switches between photodiode, opto-synaptic and opto-neuronal modes, enabling low-power, scalable in-sensor optoelectronic computing for static, dynamic and spike-based vision tasks.

High-temperature cuprate superconductors have become the testbed for state-of-the-art technologies in condensed matter physics. A great variety of advanced experimental techniques and theoretical methods have been employed to understand the mechanism of high-temperature superconductivity and to discover novel physical phenomena due to strong correlation effects. The advent of two-dimensional (2D) materials such as graphene has been accompanied by the rapid development in sample preparations, such as van der Waals mechanical exfoliation, transfer, and stacking. These emerging techniques have also benefited the study of high-temperature superconductors, particularly the bismuth-based cuprates such as Bi2Sr2CaCu2O8+delta (Bi-2212), leading to a series of important progresses and breakthroughs. They include the establishment of robust high-temperature superconductivity consistent with bulk behavior in the 2D limit and the construction of atomically flat twisted Josephson junctions. This review is organized in the following manner. First, we briefly introduce the structure of bismuth-based cuprates, showing their layered configuration of superconducting CuO2 planes alternating with insulating charge reservoir layers. The weak van der Waals bonding between BiO plane layers enables mechanical exfoliation. This structural feature provides the foundation for obtaining atomically thin flakes. Second, we provide an overview of the experimental techniques for preparing devices with ultrathin Bi-2212. They include mechanical exfoliation under inert atmospheres, protection by hexagonal boron nitride, employment of pre-patterned electrodes, and van der Waals transfer at cryogenic temperatures. These processes are necessary because Bi-2212 in the ultrathin form degrades in air and tends to lose interstitial oxygen rapidly at room temperature. The technical improvements help minimize the sample degradation, leading to a progressive enhancement in device quality and allowing for novel manipulation. These techniques eventually allow researchers to obtain monolayer Bi-2212 samples with a superconducting transition temperature that is the same as that in the bulk. It demonstrates that high-temperature superconductivity is largely captured in the 2D limit. In the third section, we summarize the diverse experimental approaches to modulate ultrathin Bi-2212 and the emergent quantum behaviors in the 2D limit. We also discuss unprecedented quantum phenomena that are found in ultrathin Bi-2212. Electrostatic gating and electrochemical gating have enabled continuous control of the carrier density, allowing systematic exploration of the superconducting phase diagram. A series of intriguing phenomena have been addressed or observed, including two-dimensional quantum phase transition between a superconductor and an insulator, enhanced regime for the sign reversal of the Hall effect, Little-Parks-like oscillations in the underdoped region, and exponential decay of the vortex entropy with the transition temperature. We also discuss the Josephson tunneling in twisted Bi-2212 bicrystals, the superconducting diode effect, and single-photon detection based on Bi-2212 thin films. In the end, we outline potential future research directions. We propose that high-pressure studies can probe the interlayer coupling within a monolayer. Angular resolved photoemission spectroscopy with micrometer spatial resolution can investigate the Fermi surface reconstruction and pseudogap evolution in the 2D limit. Nitrogen-vacancy (NV) center magnetometry can help reveal the vortex dynamics. These emerging experimental approaches, combined with vdW-based fabrication, are expected to deepen our understanding of the interplay between dimensionality and strong correlations in cuprate superconductors.

Photonic computing offers an energy-efficient, high-bandwidth platform for artificial intelligence (AI) but currently faces scalability bottlenecks stemming from depth-dependent designs, linear optical structures, and intrinsic optical losses, along with high hardware and reconfiguration costs for multi-task processing. Here, we present a scaling paradigm that circumvents these limitations by expanding network width rather than depth, leveraging the intrinsic parallelism of photonics. We implement a scalable Photonic Mixture-of-Experts (PMoE) architecture, where parallel photonic cores function as expert networks. By dynamically routing inputs to these experts, the PMoE efficiently executes multi-task workloads without altering the physical optical weights. We fabricated a PMoE chip integrating three collaborative diffraction-based expert networks, featuring 18 parallel kernels within a compact intrinsic computational-core footprint of 0.067 mm2. Experimentally, the PMoE chip achieves multi-domain image classification with an average accuracy of 97.1%. While offering further scalability, this approach outperforms conventional optical networks and reduces digital parameter overhead by 67%. Our work underscores the scalability and efficiency of the PMoE architecture for next-generation large-scale photonic AI processors.

Charging energy (EC) is essential in quantum dot (QD) devices. Previous studies on PbTe QDs have reported both the presence and absence of EC. To resolve this ambiguity, we vary the QD size, i.e., the cross-sectional area of PbTe nanowires, and track the evolution of EC. For large cross-sectional areas (' 16 000 nm2), the PbTe QDs exhibit no measurable EC, while quantized levels are well resolved. Decreasing this area successively to 5000, 1500, and 460 nm2, EC becomes finite and increases to 80, 160, and 210 & micro;eV, respectively. We further demonstrate the strong tunability of local gates, which can tune the PbTe device from the QD regime to the regime of ballistic transport. These results address concerns regarding the large dielectric constant of PbTe and provide key insights in engineering advanced PbTe quantum devices.

Complex reactions occurring at the electrode/electrolyte interface in Li metal batteries lead to the formation of a solid-electrolyte interphase (SEI), which plays a vital role in stabilizing batteries' performance. An in-depth understanding of the SEI formation mechanism thus is crucial for battery optimization. Kinetic Monte Carlo (KMC) simulations can offer detailed information on the interfacial reaction processes over multiple time scales. However, conventional KMC methods employing on-lattice models and predefined event lists are inappropriate for modeling SEI formation due to the complex chemical environment at materials interfaces. In this work, we propose an off-lattice on-the-fly KMC (OTF-KMC) method integrated with a machine learning force field (MLFF) to investigate SEI growth at the atomic level. We study a system comprising a Li(100) surface and an electrolyte containing the ethylene carbonate (EC) molecules and Li salts and identify various decomposition pathways of EC molecules and PF6- anions to generate the SEI. Our results show that the SEI features a spatial distribution of an inorganic inner layer near the Li electrode with organic products relatively far from the surface, aligning closely with experimental evidence. More importantly, we present a versatile and robust computational framework for modeling interfacial reactions in electrochemical systems.

We investigate the anisotropic behaviors in PbTe and PbTe-Pb hybrid nanowires. In previous studies on PbTe, wire-to-wire variations in anisotropy indicate significant device disorder, posing a serious challenge for applications. Here, we achieve reproducible anisotropy in PbTe nanowires through a substantial reduction of disorder. We then couple PbTe to a superconductor Pb, and observe a pronounced deviation in the anisotropy behavior compared to bare PbTe nanowires. This deviation is gate tunable and attributed to the spin-orbit interaction and orbital effect, controlled by charge transfer between Pb and PbTe. These results provide guidance for the controlled engineering of exotic quantum states in this hybrid material platform.

Clean one-dimensional electron systems can exhibit quantized conductance. The plateau conductance doubles if the transport is dominated by Andreev reflection. Here, we report quantized conductance observed in both Andreev and normal-state transports in PbTe-Pb and PbTe-In hybrid nanowires. The Andreev plateau is observed at 4e^2/h, twice of the normal plateau value of 2e^2/h. In comparison, Andreev conductance in the best-optimized III-V nanowires is non-quantized due to mode-mixing induced dips (a disorder effect), despite the quantization of normal-state transport. The negligible mode mixing in PbTe hybrids indicates an unprecedented low-disorder transport regime for nanowire devices, beneficial for Majorana researches.

Photonic neuromorphic computing offers substantial enhancements in machine vision processing by providing ultrahigh operation bandwidth and reduced energy consumption, thereby outperforming conventional electronic systems based on von Neumann architectures. However, scalability challenges persist in implementing chip-scale photonic computing—particularly when accommodating high-dimensional tensor inputs—due to inherent physical constraints and the complexity of control engineering. In this paper, we introduce a compact photonic neuromorphic processor that integrates with an on-chip diffractive multi-channel multi-kernel optical convolution unit (M2OCU) to enable parallel, high-complexity vision perception. By leveraging amplitude-phase co-modulation within the M2OCU, high-dimensional tensors can be simultaneously loaded and processed with energy efficiency exceeding 100 giga-operations per watt per square millimeter (GOPS/W/mm2). We experimentally validate the feasibility of M2OCU by demonstrating its application in machine vision, including photonic channel-wise pooling and video-based multi-frame fusion for human action recognition. Both applications achieve comparable accuracies while significantly reducing computational load. Our work provides a pathway for future large-scale and high-dimensional information processing with photonic integrated circuits, enabling the practical application of chip-scale photonic computing in increasingly sophisticated scenarios—such as autonomous driving, astronautics, and telecommunications—at picosecond latency and femtojoule-level energy consumption per operation.

Photoelectric memristors have shown great potential for future machine visions, via integrating sensing, memory, and computing (namely "all-in-one") functions in a single device. However, their hard-to-tune photoresponse behavior necessitates extra function modules for signal encoding and modality conversion, impeding such integration. Here, we report an all-in-one memristor with Cs2AgBiBr6 perovskite, where the Br vacancy doping-endowed tunable energy band enables tunable photoresponsivity (TPR) behavior. As a result, the memristor showed a large tunable ratio of 35.9 dB, while its photoresponsivity presented a maximum of 2.7 x 10(3) mA W-1 and a long-term memory behavior with over 10(4) s, making it suitable for realizing all-in-one processing tasks. By mapping the algorithm parameters onto the photoresponsivity, we successfully performed both recognition and processing tasks based on the TPR memristor array. Remarkably, compared with conventional complementary metal-oxide-semiconductor counterparts, our demonstrations provided comparable performance but had similar to 133-fold and similar to 299-fold reductions in energy consumption, respectively. Our work could facilitate the development of all-in-one smart devices for next-generation machine visions. image

We present an integrated, reconfigurable diffractive convolutional processor based on a hard-parameter sharing algorithm. This enhanced optoelectronic system performs 12-kernel parallel convolutions, achieving a 73% reduction in power-intensive digital computations.

Relaxor ferroelectric thin films are recognized for their ultrahigh power density, rendering them highly promising for energy storage applications in electrical and electronic systems. However, achieving high energy storage performance with chemically homogeneous, environmentally friendly and compositionally stable materials remains challenging. In this work, we present a design of dielectrics with high energy storage performance via an in-plane polar domains incorporating polar nanoregions mechanism. Guided by phase-field simulations, we synthesized La/Si co-doping BaTiO3 solid-solution thin films with high chemical homogeneity to realize high energy storage performance. Given that, we achieve a high energy density of 203.7J/cm3 and an energy efficiency of approximately 80% at an electric field of 6.15MV/cm. This mechanism holds significant promise for the design of next-generation high-performance dielectric materials for energy storage and other advanced functional materials.

Semiconductor nanowires coupled to a superconductor provide a powerful testbed for quantum device physics such as Majorana zero modes and gate-tunable hybrid qubits. The performance of these quantum devices heavily relies on the quality of the induced superconducting gap. A hard gap, evident as vanishing subgap conductance in tunneling spectroscopy, is both necessary and desired. Previously, a hard gap has been achieved and extensively studied in III-V semiconductor nanowires (InAs and InSb). In this study, we present the observation of a hard superconducting gap in PbTe nanowires coupled to a superconductor Pb. The gap size ($\Delta$) is $\sim$ 1 meV (maximally 1.3 meV in one device). Additionally, subgap Andreev bound states can also be created and controlled through gate tuning. Tuning a device into the open regime can reveal Andreev enhancement of the subgap conductance, suggesting a remarkable transparent superconductor-semiconductor interface, with a transparency of $\sim$ 0.96. These results pave the way for diverse superconducting quantum devices based on PbTe nanowires.

Degeneracy and symmetry have a profound relation in quantum systems. Here, we report gate-tunable subband degeneracy in PbTe nanowires with a nearly symmetric cross-sectional shape. The degeneracy is revealed in electron transport by the absence of a quantized plateau. Utilizing a dual gate design, we can apply an electric field to lift the degeneracy, reflected as emergence of the plateau. This degeneracy and its tunable lifting were challenging to observe in previous nanowire experiments, possibly due to disorder. Numerical simulations can qualitatively capture our observation, shedding light on device parameters for future applications.

Network structures by semiconductor nanowires hold great promise for advanced quantum devices, especially for applications in topological quantum computing. In this study, we created networks of PbTe nanowires arranged in loop configurations. Using shadow-wall epitaxy, we defined superconducting quantum interference devices (SQUIDs) using the superconductor Pb. These SQUIDs exhibit oscillations in supercurrent upon the scanning of a magnetic field. Most of the oscillations can be fitted assuming a sinusoidal current-phase relation for each Josephson junction. Under certain conditions, the oscillations are found to be skewed, suggesting possible deviation from a sinusoidal behavior. Our results highlight the potential of PbTe nanowires for building complex quantum devices in the form of networks.

Planar Josephson junctions are predicted to host Majorana zero modes. The material platforms in previous studies are two dimensional electron gases (InAs, InSb, InAsSb and HgTe) coupled to a superconductor such as Al or Nb. Here, we introduce a new material platform for planar JJs, the PbTe-Pb hybrid. The semiconductor, PbTe, was grown as a thin film via selective area epitaxy. The Josephson junction was defined by a shadow wall during the deposition of the superconductor Pb. Scanning transmission electron microscopy reveals a sharp semiconductor-superconductor interface. Gate-tunable supercurrent and multiple Andreev reflections are observed. A perpendicular magnetic field causes interference patterns of the switching current, exhibiting Fraunhofer-like and SQUID-like behaviors. We further demonstrate a prototype device for Majorana detection, wherein phase bias and tunneling spectroscopy are applicable.

Here, we demonstrate a high-performance photodetector for wide spectral detection (405 nm-808 nm). The device is based on 2D/3D van der Waals heterojunction (vHJ) by directly integrating MAPbI 3 single crystal (SC) onto monolayer graphene with a developed inverse temperature crystallization method (DITC). The proposed photodetector exhibits a high responsivity of 1.45 A/W, an excellent detectivity of 6.67×10 12 Jones, an impressive on/off ratio of nearly 10 4 . Moreover, the fabricated device also shows an excellent self-powered behavior with a high 3-dB bandwidth of 3.5 kHz, and a fast rise/fall time of 69 μs/138 μs. Our work provides a novel insight for the design of high-performance photodetector based on 2D/3D vHJ.

Optical computing is regarded as one of the most promising computing paradigms for solving the computational bottleneck and accelerating artificial intelligence in the post-Moore age. While reconfigurable optical processors make artificial general intelligence (AGI) possible, they often cannot process multimodal signals. Here, we propose an integrated all-optical multimodal learning engine (AOMLE) built by reconfigurable phase-change meta-atoms. The engine architecture can be mapped to different optical neural networks by laser direct writing for phase-change materials, enabling more efficient processing of visual and auditory information at the speed of light. The AOMLE provides a cutting-edge idea for reconfigurable optical processors with increasing demands for complicated AI models.

This paper presents the design and implementation of a system for real-time measurement and estimation of Insulated Gate Bipolar Transistor (IGBT) junction temperature. A specialized circuit is developed to accurately discern the turn-off delay time of the IGBT. Through experimentation, the relationship between the turn-off delay time and junction temperature, switch current, and switch voltage is characterized. Further-more, the system's offline junction temperature measurement accuracy is validated using dual-pulse testing. Based on RC ladder network, comprehensive models for device losses and thermal resistances are established. Existing IGBT junction temperature monitoring systems often lack real-time verification under continuous operation scenarios. To address this, we construct a dedicated testing platform based on Fuji's half-bridge module 2MBI300XNA120-50, facilitating real-time monitoring of device junction temperature within each switching cycle. The efficacy of the proposed junction temperature measurement and estimation system is evaluated through comparative analysis.