Altermagnetism arises from composite real-space and spin-space symmetries, combining zero net magnetization with pronounced momentum-dependent spin splitting. This review highlights the pivotal role of angle-resolved photoemission spectroscopy (ARPES)—along with its spin-resolved (SARPES) and circular-dichroism (CD-ARPES) variants, in directly visualizing the nonrelativistic band splitting and spin textures of altermagnets. Within the spin-group framework, we distinguish ferromagnetic, antiferromagnetic, and altermagnetic orders and elucidate the symmetry origin of spin polarization. We then systematically review representative systems: the debated $$d$$ -wave prototype RuO2, layered $$d$$ -wave altermagnets KV2Se2O and Rb1−δ V2Te2O, and a series of $$g$$ -wave compounds, including MnTe (domain-tunable) and CrSb (topological), together with the noncoplanar antiferromagnet MnTe2 and other emerging and prospective candidates and platforms. Overall, ARPES has become a key microscope for resolving symmetry-driven spin splitting. Future advances in micro/nano-beam and in-situ spectroscopies, combined with strain and domain engineering, heterostructure design, and the exploration of broader unconventional magnetic states, are expected to drive the joint evolution of altermagnetism and photoemission spectroscopy, paving the way for spintronic and correlated quantum research.

Ferroelectric memories have undergone a transformative evolution from conventional perovskite-based materials to modern fluorite-structured ferroelectrics, driven by the pursuit of scalable, low-power, and CMOS-compatible non-volatile memory solutions. The observation of ferroelectricity in nanoscale HfO2-based films has enabled integration with CMOS-compatible processes, providing advantages such as potential scalability, low power consumption, and non-volatility, while facilitating continued scaling and high-density integration. Leveraging established materials infrastructure in the semiconductor industry, hafnia–based ferroelectrics have been incorporated in various memory architectures, including ferroelectric random-access memory (FeRAM), ferroelectric tunnel junctions (FTJs), ferroelectric field-effect transistors (FeFETs), and ferroelectric memcapacitors (FeCAPs). Beyond conventional non-volatile storage, these devices have also emerged as promising building blocks for in-memory computing applications, including neuromorphic systems, hardware security primitives, and associative memory. In this review, we explore the historical development of ferroelectric memories from a materials–device co-design perspective, examine recent advances in device architectures and in-memory computing applications, and discuss the remaining challenges in endurance, retention, variability, and scaling. Finally, we propose future research directions that integrating material innovation, interface engineering, and circuit-level optimization to realize the full potential of ferroelectric memories in next-generation computing platforms.Graphical abstract

Complementary logic and memory circuits based on n-type indium gallium zinc oxide (IGZO) and p-type tin monoxide (SnO) thin-film transistors (TFTs) were demonstrated with low-voltage, hysteresis-free operation. Optimization of IGZO channel thickness precisely tuned the inverter switching point to near VDD/2, achieving a high voltage gain of 146.6 V/V at VDD = 3 V and ultra-low static power consumption in the nanowatt range. SU-8 passivation effectively suppressed bias-stress-induced degradation in both IGZO and SnO TFTs, enhancing long-term stability and reducing device variation. Using these optimized devices, a 3-stage ring oscillator exhibited stable oscillations, and 6T-SRAM cells achieved tunable static noise margins by adjusting transistor strength ratios. This work represents one of the first implementations of IGZO/SnO-based 6T-SRAM and demonstrates the potential of oxide semiconductor complementary circuits for low-power and reliable system integration, with promising future applicability to non-volatile memory and on-device artificial intelligence hardware.Graphical abstract

By precisely timed optical excitation of their spin, optical emitters such as semiconductor quantum dots or atoms can be harnessed as sources of linear photonic cluster states. This significantly reduces the required resource overhead to reach fault-tolerant optical quantum computing. Here, we develop an algorithm that analytically tracks the global density matrix through the process of the protocol for generating linear-cluster states by Lindner and Rudolph. From this we derive a model to calculate the entangling gate fidelity and the state fidelity of the generated linear optical cluster states. Our model factors in various sources of error, such as spin decoherence and the finite excited state lifetime. Additionally, we highlight the presence of partial reinitialization of spin coherence with each photon emission, eliminating the hard limitation of coherence time. Our framework provides valuable insight into the cost-to-improvement trade-offs for device design parameters as well as the identification of optimal working points. For a combined state-of-the-art quantum dot with a spin coherence time of {T}_{2}^{*}=535 ns and an excited state lifetime of tau =23 ps, we show that a near-unity entangling gate fidelity as well as near-unity state fidelity for 3-photon and 7-photon linear cluster states can be reached.Graphical

This study investigates the influence of sputtering plasma-induced damage on stochastic characteristics in HfZrO₂ (HZO)-based ferroelectric tunnel junctions (FTJs), with an emphasis on memory and neuromorphic device optimization. Variation of the sputtering plasma power during top electrode deposition introduces distinct levels of trap within the HZO layer. Low-frequency noise (LFN) spectroscopy and temperature-dependent electrical measurements confirm that higher plasma power generates additional shallow-level traps, thereby promoting Poole-Frenkel conduction while simultaneously increasing current noise magnitude. Although the resulting enhancements in on-current density and ferroelectric tunnel electroresistance (TER) ratio are beneficial for high-density memory integration, these conditions also elevate stochastic fluctuations, potentially degrading read margins and long-term endurance. Furthermore, the observed increase in stochasticity negatively affects neuromorphic inference accuracy, particularly after endurance cycling stress. These results demonstrate the critical interplay among plasma process conditions, trap density, and LFN in FTJs. By systematically engineering sputtering process parameters, we optimize the electrical performance with minimized stochastic noise. This approach provides guidelines for the development of next-generation ferroelectric-based memories and neuromorphic systems with consideration of stochasticity, where robust performance and reliability are imperative for large-scale integration.Graphical abstract

A surface-enhanced Raman scattering (SERS)-based aptasensor was developed for the rapid and sensitive detection of Escherichia coli (E. coli), a major pathogen in urinary tract infections (UTIs). The sensor utilizes magnetic beads embedded with gold nanoparticles (MB-AuNPs) functionalized with capture DNA (cDNA) as both the SERS-active substrate and magnetic separation tool. The detection mechanism relies on an aptamer DNA-probe DNA complex: when the aptamer binds specifically to E. coli, the probe DNA is released and subsequently hybridizes with cDNA on the MB-AuNPs. This brings a Cy5 Raman label close to the gold surface, generating a strong SERS signal. The assay offers a one-step process, eliminating the need for bacterial culture or nucleic acid amplification, and completes within approximately 6h. Quantitative analysis demonstrated a detection limit of 5.9 × 103CFU/mL, well below the clinical threshold for UTIs, with a reliable calibration curve (R2 = 0.990). Selectivity tests confirmed high specificity for E. coli without cross-reactivity to other bacteria. Clinical evaluation using 21 urine samples showed high diagnostic performance: 100% sensitivity, 91% specificity, 95% accuracy, and 100% precision compared to standard urine culture. These results highlight the aptasensor's potential as a rapid, sensitive, and specific alternative for UTI diagnosis in clinical settings.

Single emitters in solid state are promising sources of single and entangled photons. To boost their extraction efficiency and tailor their emission properties, they are often incorporated in photonic nanostructures. However, achieving accurate and reproducible placement inside the cavity is challenging but necessary to ensure the highest mode overlap and optimal device performance. For many cavity types —such as photonic crystal cavities or circular Bragg grating cavities — even small displacements lead to a significantly reduced emitter-cavity coupling. For circular Bragg grating cavities, this yields a significant reduction in Purcell effect, a slight reduction in efficiency and it introduces polarization on the emitted photons. Here we show a method to achieve high accuracy and precision for deterministically placed cavities on the example of circular Bragg gratings on randomly distributed semiconductor quantum dots. We introduce periodic alignment markers for improved marker detection accuracy and investigate overall imaging accuracy achieving (9.1 ± 2.5) nm through image correction. Since circular Bragg grating cavities exhibit a strong polarization response when the emitter is displaced, they are ideal devices to probe the cavity placement accuracy far below the diffraction limit. From the measured device polarizations, we derive a total spatial process accuracy of (33.5 ± 9.9) nm based on the raw data, and an accuracy of (15 ± 11) nm after correcting for the system response, resulting in a device yield of 68% for well-placed cavities.

A 3D motor neuron (MN) spheroid has been developed to investigate neurodegenerative and neuromuscular junction (NMJ) disease. However, core necrosis and reduced neurogenesis, impairing neural network formation, were observed as the MN spheroid matured. In this study, to enhance neural network formation, a biohybrid MN spheroid composed of neural cells/reduced graphene oxide (rGO)/human umbilical vein endothelial cells (HUVECs) was generated for the first time and applied to 3D biosensing system for MNJ disease. By incorporating rGO and HUVECs at the onset of human neural stem cell (hNSC) culture, rGO and HUVECs were evenly distributed within MN spheroid generated by differentiation of hNSC, which improved oxygen- and nutrient- supply by reduction of core necrosis, and enhanced neurogenesis. The fabricated biohybrid MN spheroid improved neural network formation and electrophysiological signal. This method was also applied to generate biohybrid cerebral organoids from human induced pluripotent stem cells (hiPSCs), emphasizing its versatility for diverse 3D neural models. Then, a 3D NMJ biosensing system was fabricated by positioning the biohybrid MN spheroid with muscle bundles to evaluate its utility in neuromuscular disease modeling. Biohybrid MN spheroids generated from induced pluripotent stem cells of sporadic amyotrophic lateral sclerosis (ALS) patients were used to make NMJ. Reduced contraction of the connected muscle bundle due to ALS could be restored by upon treatment with the bosutinib, ALS drug, demonstrating the potential use for drug screening. The method to generate biohybrid spheroid can be applied to generation of various biohybrid brain organoids, and the proposed 3D NMJ biosensing system can be used to drug screening of diverse neuromuscular diseases.

ZnMgO nanoparticles (ZMO NPs) are widely used as electron transport layers in optoelectronic devices such as light-emitting diodes (LEDs) and photodiodes (PDs) primarily because of their facile synthesis and excellent electron transport properties. However, the surface hydroxyl groups (‒OH) on the ZMO NPs introduce charge traps, inhibit electron transport, and reduce device stability, particularly under ambient humidity and oxygen. Therefore, in this study, an alcohol treatment (AT) method was developed to remove surface ‒OH via proton transfer to effectively reduce trap states and dipole moments and enhance surface passivation. Quantum-dot-based LEDs and PDs fabricated using the AT-based ZMO NPs exhibited improved current density, luminance, and external quantum efficiency compared to the untreated devices. Notably, the methanol-treated devices achieved an operational lifetime of approximately 28 h under ambient conditions, representing a substantial advancement in device stability and performance. The AT approach is a simple and effective strategy for optimizing the ZMO NPs for next-generation optoelectronic applications.