arXiv Machine Learning
Jul 31
Nanoparticle Networks for Neuromorphic Computing
arXiv:2607. 27844v1 Announce Type: cross Abstract: Physical computing leverages complex dynamical systems for energy-efficient data processing.
Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial $\mathrm{SrTiO_3}$ on Si memristors via Dynamic Spectral Optimization
Physics-informed neural networks (PINNs) are applied to model ion‑electronic drift‑diffusion in Pt/SrTiO₃/Si memristive heterostructures, overcoming numerical stiffness and multiscale spatial challenges. A cascaded PINN architecture with a custom Chebyshev spectral optimizer (DSO V2 Hybrid) isolates potential, carrier density, and vacancy transport into four sequential sub‑networks, avoiding condition numbers above 10¹⁶. The surrogate reproduces experimental conductive‑AFM current‑voltage hysteresis with R² > 0.96, maintains Poisson consistency, and offers differentiable inverse parameter estimation and linear‑time inference compared to conventional finite‑element solvers.
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arXiv Machine Learning
Sep 25
From Processing to Functionality: Engineering Accessible Material States in Cu-Embedded SiO$_x$ Memristive Devices
The paper develops a multiscale framework linking plasma deposition conditions to the functionality of sputtered SiO$_x$/Cu/SiO$_x$ memristive devices. By analyzing over 50,000 devices and combining plasma and atomistic simulations, it shows that device behavior arises from a probabilistic cascade of defect formation, evolution, and functional regime emergence, rather than deterministic mappings. A latent descriptor based on reconstructed oxygen‑vacancy density captures the combined effects of structural disorder and defect topology, linking hidden material properties to observable electrical responses and explaining variability in large‑area devices.
arXiv Machine Learning
Sep 4
Mesh-Native Physics-Informed Graph Surrogates for TCAD-in-the-Loop Design Space Exploration
The paper introduces a physics‑informed graph attention network that directly operates on the tetrahedral mesh used in TCAD simulations of FinFET devices. By predicting electrostatic potential and quasi‑Fermi levels at every mesh node and training with both data loss and finite‑volume current‑continuity residuals, the surrogate retains the underlying carrier‑transport physics while achieving size generalization. Benchmarks against Sentaurus Device show sub‑volt RMSE for the drift‑diffusion fields and a per‑design throughput that is orders of magnitude faster, enabling rapid Pareto‑front exploration of large multi‑fin arrays that would otherwise be prohibitively slow to simulate.
arXiv Machine Learning
Sep 15
PRISM-UDE: Physics-Regularized Iterative Symbolic Modeling of 3nm FinFETs via Universal Differential Equation
arXiv:2609.13200v1 Announce Type: cross Abstract: Compact transistor models are the mathematical backbone of circuit simulation. However, at advanced nodes such as 3nm, transport physics becomes too...
arXiv Machine Learning
Sep 10
Deep Learning to Automate Parameter Extraction and Model Fitting of Two-Dimensional Transistors
arXiv:2507.05134v2 Announce Type: replace Abstract: We present a deep learning approach to extract physical parameters (e.g., mobility, Schottky contact barrier height, defect profiles) of two-dimens...
arXiv AI
Jun 9
Agentic multi-fidelity learning of quasiparticle and excitonic properties
arXiv:2606. 07836v1 Announce Type: cross Abstract: Many-body GW-Bethe-Salpeter equation calculations are essential for accurate simulations of electronic structure and optical properties in modern low-dimensional nanomaterials.
arXiv Machine Learning
Jul 2
Self-Organized Learning in Oscillatory Neural Networks with Memristive Signed Couplings
arXiv:2607. 00286v1 Announce Type: cross Abstract: Oscillatory neural networks (ONNs) have emerged as a promising neuromorphic architecture, leveraging coupled dynamical systems to perform computation and represent information through phase relationships.
arXiv Machine Learning
Jun 16
Learning Hybrid Biophysical Neuron Models with Neural ODEs
arXiv:2606. 16693v1 Announce Type: cross Abstract: Biophysical neuron models link measurements of neural activity to underlying cellular mechanisms.
arXiv AI
Jun 15
A Fixed-Point Neural Operator for Size- and Functional-Transferable Hamiltonian Prediction
arXiv:2606. 14498v1 Announce Type: cross Abstract: Predicting the Kohn-Sham Hamiltonian with machine learning can accelerate density functional theory while retaining access to molecular orbitals, energy levels, and electronic-structure observables that energy-only surrogates cannot resolve.
arXiv Machine Learning
Jun 30
PCGD: Physics-Guided Conditional Graph Diffusion for TCAD Device Simulation
arXiv:2606. 29272v1 Announce Type: new Abstract: Technology computer-aided design (TCAD) semiconductor device simulation is fundamentally constrained by the high computational cost of iteratively solving coupled drift-diffusion equations.
arXiv Machine Learning
Aug 31
Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures
arXiv:2608.27533v1 Announce Type: cross Abstract: We introduce a new ab initio approach to simulate semiconductor devices that integrates scalable machine-learned (ML) electronic structure models wit...
arXiv Machine Learning
Jul 7
LRX-PINN: A Layer-Resolving XNet Physics-Informed Neural Network with Integrated Cauchy Activations for Convection-Dominated Problems
arXiv:2607. 03682v1 Announce Type: cross Abstract: Convection-dominated convection-diffusion problems often develop thin layers, where the solution has sharp transition profiles and its derivatives are highly localized.