ABSTRACT Emerging technologies such as artificial intelligence (AI), large-scale molecular simulations, and next-generation materials and drug discovery demand unprecedented computational power and high-bandwidth interconnects. Conventional two-dimensional integrated circuits (2D ICs) are nearing their physical and performance limits due to challenges in thermal management, signal integrity, and leakage currents. Three-dimensional integrated circuits (3D ICs) offer a promising pathway to overcome these limitations by vertically stacking multiple device layers, thereby reducing interconnect lengths and enabling a broad range of high-performance applications. This paper presents a comprehensive review of 3D ICs systems, with an emphasis on energy efficiency across various conventional and heterogeneous integration schemes, packaging architectures, and the trade-offs between thermal and electrical performance. We examine the relative merits of different bonding techniques, floorplanning algorithms, cooling solutions, and power delivery network (PDN) designs, highlighting their interdependencies and optimization challenges. The insights presented in this review are intended to help researchers and designers identify and implement strategies that enhance the performance, energy efficiency, and reliability of 3D ICs for high-performance computing applications.

Achromatic multilevel diffractive lenses (AMDLs) represent a breakthrough in flat optics by overcoming chromatic aberration with practical manufacturability. This review examines their evolution from concepts to state-of-the-art devices (2016–2025), analyzing design methods, fabrication, limitations, and applications. We trace the development from zone plates to free-form AMDLs, highlighting the shift from analytical to computational design. The review explores inverse optimization algorithms like direct binary search and global optimization for achieving broadband achromatic performance. Manufacturing advances are categorized, from lithography for centimeter-scale devices to emerging techniques such as 3D printing and laser direct writing. Theoretical analysis establishes fundamental trade-offs between lens diameter, numerical aperture, bandwidth, and efficiency. We compare AMDLs with achromatic metalenses to outline the advantages and trade-offs of each paradigm. Furthermore, we examine the integration of artificial intelligence in AMDL design, from correction algorithms to end-to-end optimization. This synergy of computational optics, advanced manufacturing, and machine learning enables compact, high-performance optical systems for consumer electronics, scientific instrumentation, and photonic technologies, providing engineers with key insights and future research directions.

ABSTRACT Thermoelectric (TE) materials have garnered significant interest due to their capacity to convert heat directly into electrical energy and vice versa, offering a sustainable route for energy harvesting and waste heat recovery. Nevertheless, many of the high-performance TE materials reported to date rely on elements that are scarce, costly, or environmentally hazardous, thereby limiting their large-scale deployment. To overcome these challenges, the development of efficient, earth-abundant, and environmentally benign alternatives is essential. Although first-principles methods provide valuable insights into the transport behavior of potential TE materials, their high computational cost restricts their utility in large-scale material screening. Recent progress in computational infrastructure, along with the advent of data-centric approaches such as machine learning (ML), has transformed the landscape of thermoelectric research. ML algorithms, trained on comprehensive datasets including experimental measurements, crystallographic data, and density functional theory (DFT) results can predict key TE metrics, such as the figure of merit (ZT), with remarkable speed and accuracy. This review explores the integration of ML into TE materials discovery, emphasizing its role in property prediction, descriptor engineering, and structural optimization. A systematic examination of ML-driven strategies promises to accelerate the discovery process and improve the efficiency of next-generation thermoelectric systems.

ABSTRACT The utilization of Ramsey interferometry via coherent population trapping (CPT), which enables the optical interrogation of microwave transitions without the reliance on microwave cavities, plays a crucial role in the advancement of compact and low-power quantum sensors. Particularly, high-resolution CPT-Ramsey spectroscopy of alkali-metal atoms has found extensive application in constructing miniaturized quantum sensors like atomic magnetometers and atomic clocks. Due to their well-defined environment and extended coherence time, cold atoms offer enhanced sensitivity and precision in CPT-Ramsey interferometry. This review comprehensively examines CPT-Ramsey interferometry with cold atoms, encompassing both conventional and multi-pulse techniques, and explores their potential applications in practical quantum sensors.

ABSTRACT Inertial particle microfluidics (IPMF) is an emerging technology for the manipulation and separation of microparticles and biological cells. Since the flow physics of IPMF is complex and experimental studies are often time-consuming or costly, computer simulations can offer complementary insights. In this tutorial review, we provide a guide for researchers who are exploring the potential of the lattice-Boltzmann (LB) method for simulating IPMF applications. We first review the existing literature to establish the state of the art of LB-based IPMF modelling. After summarising the physics of IPMF, we then present related methods used in LB models for IPMF and show several case studies of LB simulations for a range of IPMF scenarios. Finally, we conclude with an outlook and several proposed research directions.

ABSTRACT Quantum machine learning is a rapidly growing field at the intersection of quantum technology and artificial intelligence. This review provides a two-fold overview of several key approaches that can offer advancements in both the development of quantum technologies and the power of artificial intelligence. Among these approaches are quantum-enhanced algorithms, which apply quantum software engineering to classical information processing to improve keystone machine learning solutions. In this context, we explore the capability of hybrid quantum-classical neural networks to improve model generalization and increase accuracy while reducing computational resources. We also illustrate how machine learning can be used both to mitigate the effects of errors on presently available noisy intermediate-scale quantum devices, and to understand quantum advantage via an automatic study of quantum walk processes on graphs. In addition, we review how quantum hardware can be enhanced by applying machine learning to fundamental and applied physics problems as well as quantum tomography and photonics. We aim to demonstrate how concepts in physics can be translated into practical engineering of machine learning solutions using quantum software.

ABSTRACT Advances in FEL technologies have contributed remarkably to various scientific fields over the past decade, and ultrafast molecular dynamics is no exception. The ability to probe motions of the molecule via scattering provides uniquely direct structural information, which, when combined with traditional spectroscopic techniques of comparable temporal resolution, paints a holistic movie of the molecular dynamics. This review aims to provide an introduction to the ultrafast scattering of gas-phase molecules, and to identify the key results and technological breakthroughs that advance our acquaintance of ultrafast molecular dynamics, with a particular focus on the achievements in ultrafast molecular dynamics since the first generation of FEL facilities. We present a brief history of gas-phase ultrafast scattering and the fundamentals of electron- and x-ray scattering, highlighting the complementarity, differences, and bottlenecks of the two experimental scattering methods. We then consider key upgrades in XRS and UED experiments that facilitated the unprecedented spatiotemporal resolution that enabled many of the notable results in the field. Finally, we examine anticipated facility upgrades that address the demand for experimental versatility and enable further developments and exploration.

ABSTRACT The deformation of interatomic distances with respect to those of the perfect crystal generates atomic-level strain. In nanoalloys, strain can arise because of finite size, morphology, domain structure and lattice mismatch between their atomic compounds. Strain can strongly affect the functional properties of nanoalloys, as it alters their electronic energy levels. Moreover, atomic-level strain generates atomic-level stress, which in turn results in distortions induced by strain. When the stress accumulated in a nanoalloy exceeds a certain level, the particle can relax that stress by undergoing structural transitions such as shape and/or chemical ordering transitions. Atomic-level strain is then a powerful tool to control and manipulate the structural and functional properties of nanoalloys. This requires a combined theoretical and experimental approach both to deeply understand the physical origin of strain, and to characterize it with a sub-angstrom resolution. Here, we present a theoretical analysis of the main sources of strain in nanoalloys, we analyse how atomic-level strain can be experimentally measured with transmission electron microscopy, we discuss its effect on the functional properties of nanoalloys, finally we describe how atomic-level stress arises from atomic-level strain, and how stress can induce structural transformations at the nanoscale.

ABSTRACT Bismuth halogenides, a family of quasi-one-dimensional (1D) materials, including α and α phases of Bi4Br4 and Bi4I4, have been predicted to exhibit rich and interesting topological properties. The single layer of Bi4Br4 was demonstrated to be a quantum spin Hall insulator (QSHI) with a 0.18 eV gap. Such a band gap is large enough for the observation of QSHI at room temperature. Bulk α-Bi4Br4 was categorized as a higher-order topological insulator and was soon examined in experiments. In addition, the α-Bi4Br4 exhibit simultaneously the topological phase and superconductive phase under 3.8–4.3 GPa pressure. While the single layer of Bi4I4 was shown to be close to the critical point of the QSHI/trivial-insulator phase transition, the α-Bi4I4 was considered to be a strong or weak topological insulator. In this work, we reviewed the recent progress in the topological properties of bismuth halogenides, including the theoretical calculations, angle-resolved photoemission spectroscopy, scanned tunneling microscopy analyses, quantum transport measurement and the superconducting phase transfer under pressure. We expect further research of this family material about the non-trivial superconductor and possible Majorana, room-temperature quantum transport effect and potential application in the quantum device for the electronics and information technology.

ABSTRACT Since the initial isolation of few-layer graphene, a plethora of two-dimensional atomic crystals has become available, covering almost all known materials types including metals, semiconductors, superconductors, ferro- and antiferromagnets. These advances have augmented the already existing variety of two-dimensional materials that are routinely realized by quantum confinement in bulk-semiconductor heterostructures. This review focuses on the type of material for which two-dimensional realizations are still being actively sought: magnetoelectrics. We present an overview of current theoretical expectation and experimental progress towards fabricating low-dimensional versions of such materials that can be magnetized by electric charges and polarized electrically by an applied magnetic field – unusual electromagnetic properties that could be the basis for various useful applications. The interplay between spatial confinement and magnetoelectricity is illustrated using the paradigmatic example of magnetic-monopole fields generated by electric charges in or near magnetoelectric media. For the purpose of this discussion, the image-charge method familiar from electrostatics is extended to solve the boundary-value problem for a magnetoelectric medium in the finite-width slab geometry using image dyons, i.e. point objects having both electric and magnetic charges. We discuss salient features of the magnetoelectrically induced fields arising in the thin-width limit.