arXiv subjects

Heng Wu

Publications and source records attributed to Heng Wu.

At least 19 recordsLinked to original sources

arXiv2026

Unveiling the Scaling Potential of Drain Merge through Active (DMtA) in CFETs: Breaking the Super-Via Bottlenecks and Unlocking New PPA Boosters

Drain merge (DM), a super via vertically connecting the common S/D terminals of stacked n/pFETs in Complementary FETs (CFETs), blocks further parasitic optimization and cell scaling. For the first time, this work systematically investigates the state-of-the-art Drain Merge through Active (DMtA), a revolutionary technology reported recently with the DM embedded in the active region, through a comprehensive DTCO framework spanning process integration, contact-configuration-dependent (CTCD) compact modeling, standardcell design, RO evaluation and block-level PPA benchmark on a 32-bit RISC-V Ibex core. By reducing DM parasitics and enabling DM-width optimization, DMtA improves RO frequency by 11.7% over its conventional Drain Merge through field (DMtF) counterpart. Active widening and Area Borrowing, the latter first reported in [8] and exploiting spatial slack in adjacent cells to further enlarge the nanosheet width (WNS), increase the maximum Ibex-core frequency by up to 34.8%. More importantly, DMtA also enables the once GAA-exclusive Hyper-cells on CFETs by merging the active regions across adjacent cell rows, providing a further 8.7% frequency gain. A post-routing floating-output-pin-aware optimization further removes redundant S/D contacts (CTs) and reduces power by 5.3%. Finally, DMtA facilitates more area-efficient 2.5T cell scaling by preserving single-row cell compatibility, reducing post-PR core area by 25.7%.

cond-mat.mes-hall↗

arXiv2026

Global Survey of Technologies and Industrial Applications of Grid Forming Energy Storage Systems

Grid-forming (GFM) energy storage system (ESS) is a key enabler for stabilizing future power systems with high penetration of converter-based resources (CBRs). To get a better overview of the state-of-the-art and challenges for implementing and deploying GFM-ESS, a global survey has been initiated by Cigre Working Group B4.101 - industrial implementation and application of grid forming energy storage systems. Feedback was collected from universities, transmission system operators (TSOs), power plant developers, original equipment manufacturers (OEMs), research institutes, as well as consultants. It is interesting to note that while many common understandings have been established in practice, certain gaps persist among different stakeholders. This article intends to bridge this gap by presenting a summary of the survey, including the questionnaire, responses from various stakeholders, and in-depth analysis of the survey results. The key challenges faced by different stakeholders in deploying GFM-ESS are identified, shedding light on future research in this direction.

eess.SY↗

arXiv2026

Identification and Structural Characterization of Twisted Atomically Thin Bilayer Materials by Deep Learning

Two-dimensional materials are expected to play an important role in next-generation electronics and optoelectronic devices. Recently, twisted bilayer graphene and transition metal dichalcogenides have attracted significant attention due to their unique physical properties and potential applications. In this study we describe the use of optical microscopy to collect the color space of chemical vapor deposition (CVD) molybdenum disulfide ($\mbox{MoS}_2$), and the application of a semantic segmentation convolutional neural network (CNN) to accurately and rapidly identify thicknesses of $\mbox{MoS}_2$ flakes. A second CNN model is trained to provide precise predictions on the twist angle of CVD-grown bilayer flakes. This model harnessed a dataset comprising over 10,000 synthetic images, encompassing geometries spanning from hexagonal to triangular shapes. Subsequent validation of the deep learning predictions on twist angles was executed through the second harmonic generation and Raman spectroscopy. Our results introduce a scalable methodology for automated inspection of twisted atomically thin CVD-grown bilayer.

cond-mat.mtrl-sci↗

arXiv2026

Linear-Nonlinear Fusion Neural Operator for Partial Differential Equations

Neural operator learning directly constructs the mapping relationship from the equation parameter space to the solution space, enabling efficient direct inference in practical applications without the need for repeated solution of partial differential equations (PDEs) -- an advantage that is difficult to achieve with traditional numerical methods. In this work, we find that explicitly decoupling linear and nonlinear effects within such operator mappings leads to improved learning efficiency. This yields a novel network structure, namely the Linear-Nonlinear Fusion Neural Operator (LNF-NO), which models operator mappings via the multiplicative fusion of a linear component and a nonlinear component, thus achieving a lightweight and interpretable representation. This linear-nonlinear decoupling enables efficient capture of complex solution features at the operator level while maintaining stability and generality. LNF-NO naturally supports multiple functional inputs and is applicable to both regular grids and irregular geometries. Across a diverse suite of PDE operator-learning benchmarks, including nonlinear Poisson-Boltzmann equations and multi-physics coupled systems, LNF-NO is typically substantially faster to train than several representative neural operator baselines, while achieving comparable or improved accuracy across most tested cases. On the tested 3D Poisson-Boltzmann case, LNF-NO achieves strong accuracy while requiring substantially less training time than the three-dimensional Fourier Neural Operator and Transolver baselines.

cs.LG↗

arXiv2026

Singular Port-Hamiltonian Systems Beyond Passivity

In this paper, we investigate a class of port-Hamiltonian systems with singular vector fields. We show that, under suitable conditions, their interconnection with passive systems ensures convergence to a prescribed non-equilibrium steady state. At first glance, this behavior appears to contradict the seemingly passive structure of port-Hamiltonian systems, since sustaining a non-equilibrium steady state requires continuous power injection. We resolve this apparent paradox by showing that the singularity in the vector field induces a sliding mode that contributes effective energy, enabling maintenance of the steady state and demonstrating that the system is not passive. Furthermore, we consider regularizations of the singular dynamics and show that the resulting systems are cyclo-passive, while still capable of supplying the required steady-state power. These results clarify the role of singularities in port-Hamiltonian systems and provide new insight into their energetic properties.

eess.SY↗

arXiv2026

Operator learning on domain boundary through combining fundamental solution-based artificial data and boundary integral techniques

For linear partial differential equations with known fundamental solutions, this work introduces a novel operator learning framework that relies exclusively on domain boundary data, including solution values and normal derivatives, rather than full-domain sampling. By integrating the previously developed Mathematical Artificial Data (MAD) method, which enforces physical consistency, all training data are synthesized directly from the fundamental solutions of the target problems, resulting in a fully data-driven pipeline without the need for external measurements or numerical simulations. We refer to this approach as the Mathematical Artificial Data Boundary Neural Operator (MAD-BNO), which learns boundary-to-boundary mappings using MAD-generated Dirichlet-Neumann data pairs. Once trained, the interior solution at arbitrary locations can be efficiently recovered through boundary integral formulations, supporting Dirichlet, Neumann, and mixed boundary conditions as well as general source terms. The proposed method is validated on benchmark operator learning tasks for two-dimensional Laplace, Poisson, and Helmholtz equations, where it achieves accuracy comparable to or better than existing neural operator approaches while significantly reducing training time. The framework is naturally extensible to three-dimensional problems and complex geometries.

cs.LG↗

arXiv2025

Driving the field-free Josephson diode effect using Kagome Mott insulator barriers

Josephson junctions (JJs), devices consisting of two superconductors separated by a barrier, are of great technological importance, being a cornerstone of quantum information processing. Classical understanding of superconductor-insulator-superconductor JJs is that conventional insulator's properties, other than magnetism, do not significantly influence the junction's behavior. However, recent work on quantum material (QM) JJs - using Mott insulator Nb3Br8 - resulted in magnetic field-free non-reciprocal superconductivity, termed the Josephson diode effect (JDE), implying the QM's intrinsic properties can modulate superconductivity in non-trivial ways. To date, the underlying mechanism and dependence of the JDE on correlation strength (U/t) has not been elucidated. Here we fabricate QMJJs using correlated Kagome insulators with varying U/t, Nb3X8 (X=Cl, Br, I), observing a decreasing trend of the field-free JDE with Nb3Cl8 reaching ~48% efficiency, Nb3Br8 ~6%, and Nb3I8 having no discernible JDE, matching the trend of decreasing U/t from Cl to I and suggesting correlation in insulators drives the field-free JDE.

cond-mat.supr-con↗

arXiv2025

Supercurrent interference and its transfer in a kagome superconductor

Superconductivity represents a macroscopic quantum state notable for its rich manifestations of electronic coherence and collective behavior. Kagome materials AV3Sb5 (A= K, Cs, Rb) possess cascade intertwined quantum phases including superconductivity, symmetry-breaking charge orders, nematic orders and topological states, making them attractive materials for exploring exotic superconducting states. However, the superconducting properties and the Cooper pairing behaviors have not been fully explored and understood. In this work, by studying both the magnetoresistance and critical current behaviors in KV3Sb5 ring and pristine flakes, we reveal the charge 2e paring in KV3Sb5 although anomalous oscillations with smaller periodicity were observed, and report the intrinsic superconducting phase coherence in KV3Sb5 flakes. The former is demonstrated by the careful verification of the Little-Parks oscillations in differential resistance colormaps, and the latter indicates the existence of superconducting domains in KV3Sb5. Moreover, we observed a special phenomenon: the transfer of supercurrent interference patterns between the superconducting ring and the superconducting flake, which demonstrates the global critical current effect of the superconducting phase coherence. These findings provide new insights into the Cooper pairing behaviors in KV3Sb5 and highlight the importance of global effect of superconducting phase coherence in the understanding of the superconducting behaviors.

cond-mat.supr-con↗

arXiv2025

Monocular Depth Estimation with Global-Aware Discretization and Local Context Modeling

Accurate monocular depth estimation remains a challenging problem due to the inherent ambiguity that stems from the ill-posed nature of recovering 3D structure from a single view, where multiple plausible depth configurations can produce identical 2D projections. In this paper, we present a novel depth estimation method that combines both local and global cues to improve prediction accuracy. Specifically, we propose the Gated Large Kernel Attention Module (GLKAM) to effectively capture multi-scale local structural information by leveraging large kernel convolutions with a gated mechanism. To further enhance the global perception of the network, we introduce the Global Bin Prediction Module (GBPM), which estimates the global distribution of depth bins and provides structural guidance for depth regression. Extensive experiments on the NYU-V2 and KITTI dataset demonstrate that our method achieves competitive performance and outperforms existing approaches, validating the effectiveness of each proposed component.

cs.CV↗

arXiv2025

Mathematical artificial data for operator learning

Machine learning has emerged as a transformative tool for solving differential equations (DEs), yet prevailing methodologies remain constrained by dual limitations: data-driven methods demand costly labeled datasets while model-driven techniques face efficiency-accuracy trade-offs. We present the Mathematical Artificial Data (MAD) framework, a new paradigm that integrates physical laws with data-driven learning to facilitate large-scale operator discovery. By exploiting DEs' intrinsic mathematical structure to generate physics-embedded analytical solutions and associated synthetic data, MAD fundamentally eliminates dependence on experimental or simulated training data. This enables computationally efficient operator learning across multi-parameter systems while maintaining mathematical rigor. Through numerical demonstrations spanning 2D parametric problems where both the boundary values and source term are functions, we showcase MAD's generalizability and superior efficiency/accuracy across various DE scenarios. This physics-embedded-data-driven framework and its capacity to handle complex parameter spaces gives it the potential to become a universal paradigm for physics-informed machine intelligence in scientific computing.

cs.LG↗

arXiv2025

Impact of Grid-Forming Inverters on Protective Relays: A Perspective for Current Limiting Control Design

Grid-forming (GFM) inverters can significantly alter the fault characteristics of power systems, which challenges the proper function of protective relays. This paper gives a holistic analysis of the interaction between GFM inverter-based resources (IBRs) and the supervising elements in protective relays, including directional and phase selection elements. It is revealed that the current limiting control (CLC) that is based on the current reference saturation method, adversely affects the performance of supervising elements that rely on the negative-sequence quantities. In contrast, adopting highly inductive virtual impedance in the CLC enables a reliable operation of such elements. This finding provides insights into the design of CLC for GFM IBRs from a protection perspective. It is further found that even with a highly inductive virtual impedance, the altered virtual impedance dynamics introduced by the CLC can still lead to malfunctions of the incremental quantity-based supervising elements. These theoretical findings are corroborated by simulations and controller hardware-in-the-loop (CHIL) tests.

eess.SY↗

arXiv2025

A Protection-Interoperable Fault Ride-Through Control for Grid-Forming Inverters

Differing from synchronous generators (SGs), grid-forming inverter-based resources (GFM-IBRs) exhibit rapid variations in their output impedances during transmission line faults due to the overcurrent limitation. As a result, the source dynamics during the fault period deviate significantly from those under pre-fault conditions. This fundamental difference alters the fault responses of incremental quantities, thereby jeopardizing the reliability of the supervising elements in protective relays that are based on these quantities. To address this challenge, a protection-interoperable fault ride-through (FRT) method for GFM-IBRs is proposed. This method dynamically adjusts power control of GFM-IBRs in response to the changes in output impedance, effectively mitigating variations in source dynamics and thereby preserving the reliability of incremental quantity-based supervising elements. This method also ensures effective overcurrent limitation and transient stability of GFM-IBRs. Controller hardware-in-the-loop (CHIL) and experimental tests validate the effectiveness of the proposed method.

eess.SY↗

arXiv2025

Analysis of Power Swing Characteristics of Grid-Forming VSC System Considering the Current Limitation Mode

This paper investigates power swing characteristics of grid-forming voltage source converter (GFM-VSC) systems considering the current limitation mode in both non-inertial and inertial GFM-VSC systems. Following grid faults, non-inertial GFM-VSC systems can re-synchronize with the grid but may experience significant power swings driven by its control dynamics, while inertial GFM-VSC systems may exhibit loss of synchronization (LOS), characterized by the divergence of the output angle in the active power control loop. These behaviours are different from conventional synchronous generator (SG)-based systems, where power swings are typically characterized by physical angle deviations among power sources. Based on these findings, this paper explores the performance of traditional impedance-based swing detection schemes in GFM-VSC systems. The theoretical analysis is validated through various simulations using the PSCAD/EMTDC platform, covering both single and multi-machine system scenarios.

eess.SY↗

arXiv2025

Design Optimization of Flip FET Standard Cells with Dual-sided Pins for Ultimate Scaling

Recently, we proposed a novel transistor architecture for 3D stacked FETs called Flip FET (FFET), featuring N/P transistors back-to-back stacked and dual-sided interconnects. With dual-sided power rails and signal tracks, FFET can achieve an aggressive 2.5T cell height. As a tradeoff, the complex structure and limited numbers of M0 tracks could limit the standard cell design. As a solution, multiple innovations were introduced and examined in this work. Based on an advanced node design rule, several unique building blocks in FFET such as drain merge (DM), gate merge (GM), field drain merge (FDM) and buried signal track (BST) were investigated. Other key design concepts of multi-row, split gate and dummy gate insertion (DG) were also carefully studied, delivering around 35.6% area reduction compared with 3T CFET. Furthermore, the symmetric design of FFET has unique superiority over CFET thanks to the separate N/P logic on two sides of the wafer and their connections using DM and GM. New routing scheme with dual-sided output pins on both wafer frontside (FS) and backside (BS) was proposed for the first time. Finally, we conducted a comprehensive evaluation on complex cell design, taking AOI22 as an example. New strategies were proposed and examined. The FDM design is identified as the best, outperforming the BST and dummy gate design by 1.93% and 5.13% for the transition delay.

cond-mat.mes-hall↗

arXiv2025

A Systematic Approach for Multi-objective Double-side Clock Tree Synthesis

As the scaling of semiconductor devices nears its limits, utilizing the back-side space of silicon has emerged as a new trend for future integrated circuits. With intense interest, several works have hacked existing backend tools to explore the potential of synthesizing double-side clock trees via nano Through-Silicon-Vias (nTSVs). However, these works lack a systematic perspective on design resource allocation and multi-objective optimization. We propose a systematic approach to design clock trees with double-side metal layers, including hierarchical clock routing, concurrent buffers and nTSVs insertion, and skew refinement. Compared with the state-of-the-art (SOTA) methods, the widely-used open-source tool, our algorithm outperforms them in latency, skew, wirelength, and the number of buffers and nTSVs.

cs.AR↗

arXiv2025

Raman Forbidden Layer-Breathing Modes in Layered Semiconductor Materials Activated by Phonon and Optical Cavity Effects

We report Raman forbidden layer-breathing modes (LBMs) in layered semiconductor materials (LSMs). The intensity distribution of all observed LBMs depends on layer number, incident light wavelength and refractive index mismatch between LSM and underlying substrate. These results are understood by a Raman scattering theory via the proposed spatial interference model, where the naturally occurring optical and phonon cavities in LSMs enable spatially coherent photon-phonon coupling mediated by the corresponding one-dimensional periodic electronic states. Our work reveals the spatial coherence of photon and phonon fields on the phonon excitation via photon/phonon cavity engineering.

cond-mat.mtrl-sci↗

arXiv2025

Intrinsic exciton transport and recombination in single-crystal lead bromide perovskite

Photogenerated carrier transport and recombination in metal halide perovskites are critical to device performance. Despite considerable efforts, sample quality issues and measurement techniques have limited the access to their intrinsic physics. Here, by utilizing high-purity CsPbBr3 single crystals and contact-free transient grating spectroscopy, we directly monitor exciton diffusive transport from 26 to 300 K. As the temperature (T) increases, the carrier mobility ({\mu}) decreases rapidly below 100 K wtih a {\mu}~T^{-3.0} scaling, and then follows a more gradual {\mu}~T^{-1.7} trend at higher temperatures. First-principles calculations perfectly reproduce this experimental trend and reveal that optical phonon scattering governs carrier mobility shifts over the entire temperature range, with a single longitudinal optical mode dominating room-temperature transport. Time-resolved photoluminescence further identifies a substantial increase in exciton radiative lifetime with temperature, attributed to increased exciton population in momentum-dark states caused by phonon scattering. Our findings unambiguously resolve previous theory-experiment discrepancies, providing benchmarks for future optoelectronic design.

cond-mat.mtrl-sci↗

arXiv2025

Overlay-aware Variation Study of Flip FET and Benchmark with CFET

In this work, we carried out an overlay-aware variation study on Flip FET (FFET) considering the impact on RC parasitics induced by the lithography misalignment in backside processes, and benchmarked it with CFET in terms of the power-performance (PP) and variation sources. The iso-leakage frequency degrades up to 2.20% with layout misalignment of 4 nm. It's found that the Drain Merge resistance degrades significantly with misalignment increasing and is identified as the major variation source. Through careful DTCO with design rule optimization, the variation can be greatly suppressed, while the resistance fluctuation of the DM also drops substantially. Monte Carlo random experiments were also conducted, validating the variation reduction. Comparing with the CFET featuring self-aligned gate and much less overlay induced misalignment, fortunately, FFET's PP is still better except when misalignment reaches 8 nm, which is out of spec and nearly impossible. Considering the variabilities induced by the high aspect ratio processes, CFET still faces big challenges compared with FFET.

cond-mat.mes-hall↗