6 papers
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…