7 citations · 18 across the 10 of their papers we have counts for
11 papers
A Two-Mirror Faceted Projection System for EUV Lithography
Vasiliy A. Es'kin, Egor V. Ivanov, Olga V. Martynova
We propose an all-reflective two-mirror projection system for extreme ultraviolet (EUV) lithography operating at exposure wavelengths of ~nm (Mo/Si) and ~nm (Ru/Be), de…
Gradient-based inverse lithography for EUV masks via the waveguide method and a physics-informed neural operator
Vasiliy A. Es'kin, Egor V. Ivanov
Gradient-based inverse lithography technology~(ILT) for extreme ultraviolet~(EUV) masks is presented. A novel framework treats the differentiable waveguide method and the recently…
Dynamical Systems Theory Behind a Hierarchical Reasoning Model
Vasiliy A. Es'kin, Mikhail E. Smorkalov
Current large language models (LLMs) primarily rely on linear sequence generation and massive parameter counts, yet they severely struggle with complex algorithmic reasoning. While…
Physics-Informed Neural Systems for the Simulation of EUV Electromagnetic Wave Diffraction from a Lithography Mask
Vasiliy A. Es'kin, Egor V. Ivanov
Physics-informed neural networks (PINNs) and neural operators (NOs) for solving the problem of diffraction of Extreme Ultraviolet (EUV) electromagnetic waves from contemporary lith…
Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask
Vasiliy A. Es'kin, Egor V. Ivanov
Physics-informed neural networks (PINNs) and neural operators (NOs) for solving the problem of diffraction of Extreme Ultraviolet (EUV) electromagnetic waves from a mask are presen…
About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks
Vasiliy A. Es'kin, Alexey O. Malkhanov, Mikhail E. Smorkalov
The article is devoted to the study of neural networks with one hidden layer and a modified activation function for solving physical problems. A rectified sigmoid activation functi…
Are Two Hidden Layers Still Enough for the Physics-Informed Neural Networks?
Vasiliy A. Es'kin, Alexey O. Malkhanov, Mikhail E. Smorkalov
The article discusses the development of various methods and techniques for initializing and training neural networks with a single hidden layer, as well as training a separable ph…
Separable Physics-Informed Neural Networks for the solution of elasticity problems
Vasiliy A. Es'kin, Danil V. Davydov, Julia V. Gur'eva +2
A method for solving elasticity problems based on separable physics-informed neural networks (SPINN) in conjunction with the deep energy method (DEM) is presented. Numerical experi…
Catenary and Mercator projection
Mikhail A. Akhukov, Vasiliy A. Es'kin, Mikhail E. Smorkalov
The Mercator projection is sometimes confused with another mapping technique, specifically the central cylindrical projection, which projects the Earth's surface onto a cylinder ta…
About optimal loss function for training physics-informed neural networks under respecting causality
Vasiliy A. Es'kin, Danil V. Davydov, Ekaterina D. Egorova +3
A method is presented that allows to reduce a problem described by differential equations with initial and boundary conditions to the problem described only by differential equatio…
Multiple Scattering of Electromagnetic Waves by an Array of Parallel Gyrotropic Rods
Vasiliy A. Es'kin, Alexander V. Kudrin, Tatyana M. Zaboronkova +1
We study multiple scattering of electromagnetic waves by an array of parallel gyrotropic circular rods and show that such an array can exhibit fairly unusual scattering properties…