Lithography simulation is one of the key steps in physical verification, enabled by the substantial optical and resist models. A resist model bridges the aerial image simulation to printed patterns. While the effectiveness of learning-based solutions for resist modeling has been demonstrated, they are considerably data…
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48 results for “Lithography simulation”
Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Learningcs.LG
Paper proposes active learning for hotspot detection in VLSI design.
problem Hotspot detection in VLSI design is computationally expensive and relies on costly reference libraries.
method Active learning-based layout pattern sampling and hotspot detection flow.
result Significantly reduces lithography simulation overhead with satisfactory detection accuracy.
Paper proposes a machine learning framework for VLSI mask optimization.
problem Costly VLSI mask optimization due to complex processes.
method Heterogeneous OPC framework using machine learning.
result Demonstrates efficiency and effectiveness of the proposed framework.
The paper develops methods for monitoring TPL machine health.
problem Inaccurate and untimely maintenance of TPL systems leads to poor quality and inefficiencies.
method Physics-informed data-driven predictive models integrated with statistical approaches.
result The methods achieve high accuracy across various scenarios and conditions.
This work evaluates machine learning-based hotspot detectors on synthesized layout patterns.
problem Evaluating model robustness and generality of machine learning-based hotspot detectors.
method Developed an automatic layout generation tool to synthesize various layout patterns and tested machine learning-based detectors on these synthesized layouts.
result Machine learning-based detectors need continuous study for robustness and generality in DFM flows.
Adversarial perturbations can fool CNN-based lithographic hotspot detectors, but retraining can improve robustness.
problem Adversarial perturbations can mislead ML-based lithographic hotspot detectors.
method Proposed adversarial retraining strategy to improve robustness of CNN-based detectors.
result Adversarial retraining significantly improves robustness of CNN-based hotspot detection against perturbations.
The simulator is an R package that streamlines the process of performing simulations by creating a common infrastructure that can be easily used and reused across projects. Methodological statisticians routinely write simulations to compare their methods to preexisting ones. While developing ideas, there is a temptatio…
A new framework connects machine learning models with simulation models efficiently.
problem Interpreting complex machine learning models for real-world applications.
method Model-bridging framework using kernel mean embeddings.
result Simulations and machine learning models can be used together without high computational costs.
PAMS is a Python-based platform for simulating artificial markets.
problem Simulating complex market behaviors for research and education.
method Developed as a Python-based simulator with deep learning integration.
result Demonstrated effectiveness through agent price prediction studies.
Smartfluidnet accelerates Eulerian fluid simulation with neural networks.
problem Current neural network methods for Eulerian fluid simulation lack flexibility and generalization.
method Smartfluidnet automates model generation and dynamic switching to meet user requirements.
result Smartfluidnet achieves 1.46x and 590x speedup compared to state-of-the-art models, with better simulation quality.
Proposes a new simulator for complex arrival processes.
problem Modeling and simulating complex arrival processes with non-stationary and multi-dimensional rates.
method Integrates Monte Carlo and GANs to model a broad class of arrival processes.
result Consistent and efficient estimation of the simulator using Wasserstein distance.
Develops EB for implicit likelihoods using simulators.
problem Traditional EB assumes tractable likelihoods, SBEB handles implicit likelihoods.
method Simulation-based empirical Bayes (SBEB) connects nonparametric EB to SBI, iteratively refining EB estimates.
result SBEB improves accuracy over SBI with fixed priors.
Study assesses market simulation metrics to highlight discrepancies between real and simulated markets.
problem Lack of fidelity in market simulation methods leads to discrepancies between real and simulated market data.
method Surveyed and applied a set of reference metrics to real and simulated market data.
result Significant discrepancies remain between real and simulated markets.
ACE improves GBI for simulators by approximating cost functions, making inference more efficient.
problem Inference for misspecified simulators is overly restrictive.
method Amortized cost estimation (ACE) for Generalized Bayesian Inference (GBI).
result ACE provides accurate cost predictions and more efficient inference.
New method improves sample-efficiency in neural posterior estimation using simulator gradients.
problem High-fidelity posterior estimation with complex physical simulations is time-consuming.
method Neural Posterior Estimation (NPE) with differentiable simulators and gradient information.
result Improves sample-efficiency in posterior density estimation.
Paper presents a new validation method for simulation workflow.
problem Validation of simulation workflow is challenging and domain-dependent.
method Empirical learning-based validation procedure using AHP and machine learning.
result Validation procedure is semi-automated and efficient.
Risk-Neutral Market Simulationq-fin.CP
Simulates risk-neutral markets using neural spline flows.
problem Creating realistic risk-neutral market simulations.
method Developed a low-dimensional martingale representation and used neural spline flows for sampling.
result The calibrated simulator is closest to historical data with respect to Kullback-Leibler divergence.
Taxonomy for ML in simulations, covering patterns and algorithms.
problem Enhancing simulations using machine learning.
method Presentation of eight patterns and three algorithmic areas.
result Catalog of activities and patterns for ML integration in simulations.
Simulation of an Elevator Group Control Using Generative Adversarial Networks and Related AI Toolsstat.ML
Generative Adversarial Networks simulate elevator group control without extensive data.
problem Lack of historical real-world data for system testing.
method Used GANs to generate simulation data for a multi-car elevator system.
result GANs can be used as substitutes for expensive simulation runs, but fine-tuning is needed.
Enhances SBI accuracy with multilevel Monte Carlo for expensive simulators.
problem Limited accuracy in SBI due to expensive simulators.
method Multilevel Monte Carlo techniques for cost-effective SBI.
result Significant enhancement in SBI accuracy with fixed computational budget.
New method efficiently simulates fluid flows across various conditions.
problem High computational cost in simulating fluid flows.
method Parameter-conditioned sequential generative modeling of neural networks.
result Trained models simulate fluid flows at orders of magnitude faster than traditional methods.
Bayesian neural networks improve simulation-based inference with limited data.
problem Inaccurate inference in data-poor regimes with limited or expensive simulations.
method Bayesian neural networks for posterior approximation, accounting for computational uncertainty.
result Bayesian neural networks produce well-calibrated posteriors with few simulations.
Improved nested simulation for financial risk measurement.
problem Efficiently estimating nested risk measures in financial engineering.
method Reusing inner simulation outputs to improve efficiency and accuracy.
result The proposed approach outperforms standard nested simulation and regression methods.
The paper develops a new simulation technique for estimating conditional expectations in financial models.
problem Estimating conditional expectations in financial models with expensive simulation of endogenous variables.
method Introduces a hierarchical simulation scheme with oversimplified defaults to address variance issues.
result The hierarchical simulation technique significantly improves the success of neural net regression for conditional expectation estimation.
Building high accuracy emulators for scientific simulations with deep neural architecture searchstat.ML
Fast emulators built with neural search accelerate expensive scientific simulations.
problem Slow execution of accurate simulations limits scientific discovery.
method Neural architecture search to build accurate emulators with limited data.
result Simulations accelerated by up to 2 billion times in various scientific fields.
Study proposes a new approach for deep hedging using artificial market simulations.
problem Challenges in selecting the best model for underlying asset simulations in deep hedging.
method Proposes artificial market simulations to replicate financial market stylized facts.
result Achieves similar performance to traditional approaches without mathematical finance models.
Improved inference efficiency for complex simulations.
problem Challenges in performing inference under resource-intensive stochastic simulators.
method Active sequential neural posterior estimation (ASNPE) integrating active learning into posterior estimation.
result Improved sample efficiency with low computational overhead.
Method improves simulation accuracy by mitigating distribution shift in hybrid systems.
problem Mitigating distribution shift in machine-learning augmented hybrid simulation.
method Tangent-space regularized estimator to control distribution shift.
result Marked improvements in simulation accuracy, especially for systems with high distribution shift.
Advanced simulation paradigm of human behaviour unveils complex financial systemic projectionq-fin.TR
New simulation model predicts financial market dynamics with high accuracy.
problem Extreme difficulty in financial market projections due to human behavioural complexity.
method Agent-based modeling with a hierarchical knowledge architecture to simulate diverse human groups.
result Simulator achieves 13.29% deviation in crisis scenarios and lower mean square error under normal conditions.
New method uses low-fidelity simulations to efficiently infer parameters of high-fidelity models.
problem Challenges in inferring parameters of computationally expensive high-fidelity models.
method Multifidelity simulation-based inference using transfer learning and adaptive selection of high-fidelity parameters.
result Significant reduction in the number of high-fidelity simulations required for inference.
New emulator bridges simulators using conditional optimal transport.
problem Bridging simulators with minimal distortion.
method Flow-based approach to learn likelihood transport, COT-FM for optimal matching.
result Emulator accurately captures full correction between simulators.
Simplified derivation and simulation of Feller Diffusion.
problem Deriving the probability density function of Feller Diffusion.
method Fourier Transform and Method of Characteristics for derivation; simulation algorithms for validation.
result Confirmation of hitting time probabilities via simulation.
Learning To Simulatecs.LG
Automatically optimizes simulation parameters for machine learning.
problem Costly or hard-to-acquire training data for machine learning models.
method Reinforcement learning to control simulator parameters for data synthesis.
result Approach optimizes simulation parameters to maximize model accuracy.
Moate Simulation improves accuracy and speed of financial derivative pricing.
problem Efficiently pricing financial derivatives with high accuracy.
method Discrete time simulation of probability distributions using Moate Simulation.
result Moate Simulation provides highly accurate distributions for financial derivatives pricing.
Study shows current simulations are insufficient for optimal neural network training in cosmology.
problem Insufficient training data for neural networks in cosmological inference.
method Empirical neural scaling law and Cramer-Rao bound to forecast training simulations needed.
result Current simulation suites do not provide sufficient training data for optimal neural network performance.
G-Sim uses LLMs to build reliable simulators for complex systems.
problem Building robust simulators for critical domains like healthcare and logistics is challenging.
method Hybrid framework combining LLM-driven structural design and empirical calibration.
result G-Sim produces reliable, causally-informed simulators that handle non-differentiable and stochastic simulators.
Improved flow-based inference speeds up and boosts accuracy for complex simulations.
problem Challenging inverse problems in astronomy, such as modeling strong gravitational lens systems.
method Refines flow-based generative models with simulator feedback for posterior inference.
result Improves accuracy by 53% and speeds up inference by up to 67x.
Optimize black-box simulators with local generative models.
problem Optimizing non-differentiable, stochastic simulators with intractable likelihoods.
method Differentiable local surrogate models based on deep generative models.
result Local surrogates enable gradient-based optimization, faster than baseline methods.
Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimizationq-fin.ST
The paper proposes a framework to calibrate multi-agent simulation models from output series using Bayesian optimization.
problem Calibrating multi-agent simulation models from observable output series.
method Novel eligibility set concept, two-sample Kolmogorov-Smirnov test with Bonferroni correction, Bayesian optimization (BO), and trust-region BO (TuRBO).
result Demonstrated the efficiency of the proposed framework using numerical experiments.
Study uses RL to simulate realistic market behavior.
problem Traditional market simulators lack realistic dynamic behavior.
method Agent-based simulation with reinforcement learning agents.
result RL agents simulate realistic stylized facts and market behavior.
In the applications related to airborne radars, simulation has always played an important role. This is mainly because of the two fold reason of the unavailability of desired data and the difficulty associated with the collection of data under controlled environment. A simple example will be regarding the collection of…
MarS simulates financial markets using generative models.
problem Simulating realistic financial market effects.
method Order-level generative foundation model (LMM) for realistic, interactive, and controllable order generation.
result Strong scalability and robust realism in MarS.
This paper tackles non-identifiability in financial market simulations using multivariate time series data.
problem Non-identifiability issue in social simulation models, leading to indistinguishable simulated time series data.
method Proposes a maximization-based aggregation function to form a new calibration objective function using multiple time series features.
result Significant improvements in alleviating non-identifiability and achieving higher simulation fidelity.
Simulation-based inference tackles complex inverse problems in science.
problem Challenging inverse problems in complex simulations.
method Review and identification of forces driving the field.
result Expanding the audience to appreciate the impact on science.
Simulates multi-asset spot and option markets using normalizing flows.
problem High-dimensionality of market call prices and dynamic preservation across simulators.
method Normalizing flows for efficient low-dimensional representations, conditional invertibility for joint distribution calibration.
result Calibrated simulators maintain dynamics of each underlying and accurately represent market call prices.
Unified method for balancing simulation and data collection.
problem Finding optimal solutions with uncertain inputs.
method Bayesian Information Collection and Optimisation (BICO) procedure.
result Algorithm automatically determines optimal balance between optimisation and data collection.
Efficiently estimates marginal posteriors for complex simulations.
problem Bayesian inference in high-dimensional, intractable likelihood scenarios.
method Simulates and estimates low-dimensional marginal posteriors, using truncated indicators.
result Simulator efficiency and robustness testing of inference results.
NCS enables efficient and accurate conditional simulation for complex spatial processes.
problem Challenges in simulating from complex spatial process distributions.
method Neural diffusion models and conditional score-based diffusion.
result NCS outperforms traditional methods in efficiency and accuracy.