Dual-Polarization Optical Costas Loop for DSP-Free Homodyne Short-Reach Links
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Liu, Junqian [Electrical and Computer Engineering ↗
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Framework of compressive sensing and data compression for 4D-STEM
Four-dimensional Scanning Transmission Electron Microscopy (4D-STEM) is a powerful technique for high-resolution and high-precision materials characterization at multiple length scales, including the characterization of beam-sensitive materials. However, the field of view of 4D-STEM is relatively small, which in absence of live processing is limited by the data size required for storage. Furthermore, the rectilinear scan approach currently employed in 4D-STEM places a resolution- and signal-dependent dose limit for the study of beam sensitive materials. Improving 4D-STEM data and dose efficiency, by keeping the data size manageable while limiting the amount of electron dose, is thus critical for broader applications. Here we introduce a general method for reconstructing 4D-STEM data with subsampling in both real and reciprocal spaces at high fidelity. The approach is first tested on the subsampled datasets created from a full 4D-STEM dataset, and then demonstrated experimentally using random scan in real-space. The same reconstruction algorithm can also be used for compression of 4D-STEM datasets, leading to a large reduction (100 times or more) in data size, while retaining the fine features of 4D-STEM imaging, for crystalline samples.
4D-STEM↗
Stable Machine‐Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection‐Permitting Simulations
Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the multiscale modeling framework (MMF), which embeds a kilometer-resolution cloud-resolving model (CRM) within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning offers a unique opportunity to make MMF more accessible by emulating the embedded CRM and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including coarse-grid geography, seasonality, explicit cloud condensate and wind predictions, and land coupling. Our model demonstrates skillful online performance, achieving a 5-year zonal mean tropospheric temperature bias within 2 K, water vapor bias within 1 g/kg, and a precipitation root mean square error of 0.96 mm/day. Key factors contributing to our online performance include an expressive U-Net architecture and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Despite these advances, online diagnostics reveal persistent biases in certain regions, highlighting the need for innovative strategies to further optimize online performance.
Hu, Zeyuan [NVIDIA Corporation, Santa Clara, CA (U↗
The Heterogeneous Integration of Electronic Components
Heterogeneous integration (HI) of electronics components is broadly recognized as a powerful and crucial enabler for the continued growth of computing and communication. From 2010 onwards, the value of HI is increasingly visible in the advanced packaging used in artificial intelligence, high-performance computing, smartphones and communications product implementations. In this Perspective, we argue that HI is crucial to semiconductors and more broadly to the continued evolution of computing and communications. We use leading-edge advanced packaging examples to represent the value, advancements and opportunities for HI. To succeed, it is critical to develop comprehensive HI roadmaps that inform collaborations across the design, manufacturing and reliability spectrum between systems architects, packaging and semiconductor technologists to common goals. Although this article does not provide a full roadmap, we instead detail additional parameters for artificial intelligence, smartphone and other cellular communication devices, and their constituent building blocks including interconnects, power electronics, photonics, thermal management, reliability, modelling and co-design, to foster greater collaboration opportunities among academia, research laboratories and industry.
42 ENGINEERING↗
Broad-range tuning of ferroelectric switching of La x Bi 1−x FeO 3 epitaxial films via digital doping using off-axis co-sputtering
To investigate the scope of ferroelectric behavior in La-substituted BiFeO 3 films, La x Bi 1−x FeO 3 epitaxial films were synthesized using off-axis co-sputtering on SrTiO 3 (001) and DyScO 3 (110) substrates with a SrRuO 3 bottom electrode layer. A digital-doping deposition method was used to enable precise control and continuous tuning of La concentration in high-quality LaxBi 1−x FeO 3 films across a wide range of x = 0.05–0.60, which was systematically investigated using piezoresponse force microscopy. Robust and reversible out-of-plane ferroelectric switching has been observed up to x = 0.35, while films with x ≥ 0.37 exhibit no measurable ferroelectric behavior, indicating a sharp ferroelectric-to-paraelectric phase transition between x = 0.35 and 0.37. This represents the highest reported La concentration in LaxBi 1−x FeO 3 films that retains ferroelectric ordering, highlighting opportunities to engineer ferroelectric and multiferroic properties in complex oxide heterostructures.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Energy Efficiency Scaling for 2 Decades (EES2) Roadmap for Computing
In response to the looming crisis in global energy consumption required for advanced computing applications, the United States Department of Energy (DOE) Advanced Materials and Manufacturing Technology Office (AMMTO) is leading a multi-organizational effort to define a roadmap for energy efficiency scaling for two decades (EES2) with the aim to reduce energy use in all aspects of computation by more than a factor of 1000 in two decades. By July of 2024, over 60 organizations representing industry, academia, and the national laboratories have pledged to work in various aspects of research and development to enable energy efficiency in computing including in the development of the EES2 roadmap, with an initial public release in 2024 as the first phase of an ongoing commitment to energy-efficient and sustainable computation.
Kaarsberg, Tina [U.S. Department of Energy (DOE)]↗
Influence of sizing concentration on strength, stiffness, and porosity in textile grade carbon fiber (TCF)-Epoxy composites: Revealing inverse trends
The effect of fiber sizing (i.e., surface treatment) concentration (0 %, 1.36 %, 1.52 %, 1.94 %, and 2.13 %) on the mechanical properties (tensile, flexural, interlaminar shear strength (ILSS), and low velocity impact) of textile grade carbon fiber (TCF)-epoxy composite is examined. An inverse relationship between the strength and stiffness of the composite is observed with increased sizing concentration. The root mean square (RMS) roughness of the fiber surface increased from 17.8 nm (unsized) to 22.7 nm with 2.13 % sizing concentration. It was found that the tensile strength increased by 131 % from 221.4 ± 18.5 MPa (unsized) to 510.8 ± 28.05 MPa (for 1.36 % sizing) and further by 155 %–563.7 ± 14.95 MPa at 2.13 % sizing. On the contrary, the stiffness is initially increased by 126 % from 33.52 ± 7.80 GPa (unsized) to 75.9 ± 3.21 GPa (for 1.36 % sizing) but reduced with further increase in the sizing concentration. A single fiber pull-out test is simulated using the finite element method to validate the reverse trend in strength and stiffness. The varying sizing concentration is simulated by introducing an interface of varying thickness between fibers and matrix. Simulation results confirm that a thicker interface, corresponding to a higher sizing concentration, decreases interfacial shear stress, enhancing material strength while decreasing stiffness. The reverse trend in strength and stiffness with the sizing concentration aligns with experimental observations. In conclusion, the present study emphasizes the importance of sizing concentration for mechanical properties and provide a design criterion for customized high-strength and high-stiffness applications.
Porosity↗
Modeling CO 2 flow through faulted/fractured reservoirs using tEDFM in corner-point grids
The interest in underground CO 2 storage has increased significantly over the last decade because of the rising concern about global warming due to the growing levels of greenhouse gases in the atmosphere. Considering that CO 2 accounts for 80% of these greenhouse gases, carbon capture, utilization, and storage (CCUS) is regarded as one of the most direct approaches to achieving the net zero carbon target. Although CO 2 storage in deep saline aquifers and depleted gas reservoirs has been studied extensively, most studies use commercial simulators that model faults/fractures by simply modifying the transmissibility in the direction perpendicular to the fault surfaces. Here, this work shows that this simplistic approach ignores the accelerated flow in the directions parallel to the fault plane, leading to significantly higher leakage along the fault surface. To accurately model the flow of CO 2 in faulted reservoirs, we present the first transient embedded discrete fracture model for corner-point grids (tEDFM-CPG). By comparing the results of the tEDFM-CPG to high-resolution reference solutions, we show that this approach is accurate and efficient at predicting CO 2 flow in faulted/fractured reservoirs. Finally, this work presents the use of mixed reality (MR) to efficiently observe CO 2 gas migration in the interior of these corner-point grid systems.
25 ENERGY STORAGE↗
A High-Efficiency Delayed Update Algorithm for Evaluating Slater Determinants in Quantum Monte Carlo
For quantum Monte Carlo simulations of molecular systems or supercells with thousands of electrons, matrix operations related to Slater determinants lead the computational cost. McDaniel et al. [J. Chem. Phys. 2017, 147, 174107] proposed a delayed update algorithm to increase computational efficiency by using matrix–matrix multiplication when updating the inverse matrices of Slater determinants. However, preparing intermediate matrices for applying the Sherman–Morrison–Woodbury formula remained a bottleneck. Here, in this work, we introduce an improved algorithm for CPUs and GPUs that (1) reduces this bottleneck by iteratively updating the intermediate matrices and (2) is efficient at any acceptance ratio, with no cost for rejected moves on CPUs and minimal cost on GPUs. We show the full scheme of integrating the delayed update algorithm into a single-electron move. The high efficiency of our algorithm is demonstrated on CPUs and GPUs for a 512 atom/6144 valence electron calculation, with 12× and 2× overall speed-up compared to traditional rank-1 update schemes in diffusion quantum Monte Carlo, respectively.
Luo, Ye [Argonne National Laboratory (ANL), Argonn↗
Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations
Abstract Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superparameterization embedded in an Earth System Model (ESM). We explore three methods to construct stochastic parameterizations: (a) a single Deep Neural Network (DNN) with Monte Carlo Dropout; (b) a multi‐member parameterization; and (c) a Variational Encoder Decoder with latent space perturbation. We show that the multi‐member parameterization improves the representation of convective processes, especially in the planetary boundary layer, compared to individual DNNs. The respective uncertainty quantification illustrates that methods (b) and (c) are advantageous compared to a dropout‐based DNN parameterization regarding the spread of convective processes. Hybrid simulations with our best‐performing multi‐member parameterizations remained challenging and crash within the first days. Therefore, we develop a pragmatic partial coupling strategy relying on the superparameterization for condensate emulation. Partial coupling reduces the computational efficiency of hybrid Earth‐like simulations but enables model stability over 5 months with our multi‐member parameterizations. However, our hybrid simulations exhibit biases in thermodynamic fields and differences in precipitation patterns. Despite this, the multi‐member parameterizations enable improvements in reproducing tropical extreme precipitation compared to a traditional convection parameterization. Despite these challenges, our results indicate the potential of a new generation of multi‐member machine learning parameterizations leveraging uncertainty quantification to improve the representation of stochasticity of subgrid effects.
Behrens, Gunnar [Deutsches Zentrum für Luft‐ und R↗
Navigating the Noise: Bringing Clarity to ML Parameterization Design With O $\boldsymbol{\mathcal{O}}$(100) Ensembles
Abstract Machine‐learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high‐resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large‐scale general circulation model) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi‐automated, end‐to‐end pipeline for size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error to a Mean Absolute Error loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise.
Lin, Jerry [Department of Earth System Sciences Un↗
Unsupervised discovery of extreme weather events using universal representations of emergent organization
Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones—local causal states—capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events—hurricanes and atmospheric rivers—and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.
Rupe, Adam [Pacific Northwest National Laboratory ↗
QCD Predictions for Physical Multimeson Scattering Amplitudes
We use lattice QCD calculations of the finite-volume spectra of systems of two and three mesons to determine, for the first time, three-particle scattering amplitudes with physical quark masses. Our results are for combinations of 𝜋 + and 𝐾 + , at a lattice spacing 𝑎 = 0.063 fm, and in the isospin-symmetric limit. We also obtain accurate results for maximal-isospin two-meson amplitudes, with those for 𝜋 + 𝐾 + and 2𝐾 + being the first determinations at the physical point. Dense lattice spectra are obtained using the stochastic Laplacian-Heaviside method, and the analysis leading to scattering amplitudes is done using the relativistic finite-volume formalism. Results are compared to chiral perturbation theory and to phenomenological fits to experimental data, finding good agreement.
hadron-hadron interactions↗
Two- and three-meson scattering amplitudes with physical quark masses from lattice QCD
We study systems of two and three mesons composed of pions and kaons at maximal isospin using four CLS ensembles with 𝑎 ≈ 0.063 fm, including one with approximately physical quark masses. Using the stochastic Laplacian-Heaviside method, we determine the energy spectrum of these systems including many levels in different momentum frames and irreducible representations. Using the relativistic two- and three-body finite-volume formalism, we constrain the two- and three-meson K matrices, including not only the leading 𝑠 wave, but also 𝑝 and 𝑑 waves. By solving the three-body integral equations, we determine, for the first time, the physical-point scattering amplitudes for 3𝜋 + , 3𝐾 + , 𝜋 + 𝜋 + 𝐾 + , and 𝐾 + 𝐾 + 𝜋 + systems. These are determined for total angular momentum 𝐽 𝑃 = 0 − , 1 + , and 2 − . We also obtain accurate results for 2𝜋 + , 𝜋 + 𝐾 + , and 2𝐾 + phase shifts. We compare our results to chiral perturbation theory and to phenomenological fits.
FOS: Physical sciences↗
Di-nucleons do not form bound states at heavy pion mass
We perform a high-statistics lattice QCD calculation of the low-energy two-nucleon scattering amplitudes. To address discrepancies in the literature, the calculation is performed at a heavy pion mass in the limit that the light quark masses are equal to the physical strange quark mass, 𝑚 𝜋 = 𝑚 𝐾 ≃ 714 MeV. Using a state-of-the-art momentum space method, we rule out the presence of a bound di-nucleon in both the isospin 0 (deuteron) and 1 (di-neutron) channels, in contrast with many previous results that made use of compact hexaquark creation operators. To diagnose the discrepancy, we add such hexaquark interpolating operators to our basis and find that they do not affect the determination of the two-nucleon finite-volume spectrum, and thus they do not couple to deeply bound di-nucleons that are missed by the momentum-space operators. Furthermore, we perform a high-statistics calculation of the HAL QCD potential on the same gauge ensembles and find qualitative agreement with our main results. We conclude that di-nucleons do not form bound states at heavy pion masses and that previous identification of deeply bound di-nucleons must have arisen from a misidentification of the spectrum from off-diagonal elements of a correlation function.
Physics - Physics of elementary particles and fiel↗
New Roadmap for Microelectronics: Charting the Semiconductor Industry's Path Over the Next 5, 10, and 20 Years
In the past, the role of strategic planning for semiconductor industry was met by the International Technology Roadmap for Semiconductors (ITRS), serving as a guiding light that provided manufacturers, designers, equipment suppliers, and researchers with direction years in advance. By providing a common framework for coordination across semiconductor industry stakeholders, technology development efforts were efficient and aligned. However, the dissolution of the ITRS in 2015 left a void, leading to years of disjointed efforts. Recognizing the need for unified guidance, the industry rallied for the creation of a new strategic plan, the Microelectronics and Advanced Packaging Technologies (MAPT) Roadmap (2023) was developed, through the efforts of hundreds of industry, academic and government experts, to provide detailed strategies for achieving these goals. This comprehensive plan outlines ambitious goals for the industry's future.
HPC, Roadmap, AI, Liquid Cooling, Power, Silicon, ↗
Predicting the Future of Supercomputing
The need to solve high-complexity problems using large-scale tightly coupled computing (that is, supercomputing) continues to grow. Here, in this article, we address the needs, challenges, and opportunities for supercomputing over the next decade.
97 MATHEMATICS AND COMPUTING↗
Material Needs and Measurement Challenges for Advanced Semiconductor Packaging: Understanding the Soft Side of Science
This Perspective builds upon insights from the National Institute of Standards and Technology (NIST)-organized workshop, “Materials and Metrology Needs for Advanced Semiconductor Packaging Strategies,” held at the 35th annual Electronics Packaging Symposium in Binghamton, NY, on September 5, 2024. It outlines critical challenges and opportunities related to polymer-based “soft” materials in advanced semiconductor packaging, with emphasis on polymer science, measurement science (metrology), and the strategic development of Research-Grade Test Materials (RGTMs). These efforts, led by the NIST CHIPS team, aim to advance the fundamental understanding of structure-property-processing relationships, promote standardized guidelines and innovative methods for material characterization, and accelerate the development, qualification, and adoption of next-generation packaging materials. The Perspective also distills key insights from the panel discussion with industry experts, emphasizing the need for close collaboration among materials scientists, process engineers, and metrology experts to enable a holistic strategy, further highlighting the importance of cross-sector partnerships among industry, academia, and government to address pressing challenges in packaging materials and processes.
97 MATHEMATICS AND COMPUTING↗