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e. With recent advances in online sensing technology and high-performance computing, structural health monitoring (SHM) has begun to emerge as an automated approach to the real-time conditional monitoring of civil infrastructure. Ideal SHM strategies detect and characterize damage by leveraging measured response data to update physics-based finite element models (FEMs). When monitoring composite structures, such as reinforced concrete (RC) bridges, the reliability of FEM based SHM is adversely affected by material, boundary, geometric, and other model uncertainties. Civil engineering researchers have adapted popular artificial intelligence (AI) techniques to overcome these limitations, as AI has an innate ability to solve complex and ill-defined problems by leveraging advanced machine learning techniques to rapidly analyze experimental data. In this vein, this study employs a novel Bayesian estimation technique to update a coupled vehicle-bridge FEM for the purposes of SHM. Unlike existing AI based techniques, the proposed approach makes intelligent use of an embedded FEM model, thus reducing the parameter space while simultaneously guiding the Bayesian model via physics-based principles. To validate the method, bridge response data is generated from the vehicle-bridge FEM given a set of “true” parameters and the bias and standard deviation of the parameter estimates are analyzed. Additionally, the mean parameter estimates are used to solve the FEM model and the results are compared against the results obtained for “true” parameter values. A sensitivity study is also conducted to demonstrate methods for properly formulating model spaces to improve the Bayesian estimation routine. The study concludes with a discussion highlighting factors that need to be considered when leveraging experimental data to update FEMs of concrete structures using AI techniques.
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Fast etch recipe creation with automated model-based process optimization
A method for automated creation and optimization of multistep etch recipes is presented. Here we demonstrate how an automated model-based process optimization approach can cut the cost and time of recipe creation by 75% or more as compared with traditional experimental design approaches. Underlying the success of the method are reduced-order physics-based models for simulating the process and performing subsequent analysis of the multi dimensional parameter space. SandBox Studio™ AI is used to automate the model selection, model calibration and subsequent process optimization. The process engineer is only required to provide the incoming stack and experimental measurements for model calibration and updates. The method is applied to the optimization of a channel etch for 3D NAND devices. A reduced-order model that captures the physics and chemistry of the multistep reaction is automatically selected and calibrated. A mirror AI model is simultaneously and automatically created to enable nearly instantaneous predictions across the large process space. The AI model is much faster to evaluate and is used to make a Quilt™, a 2D projection of etch performance in the multidimensional process parameter space. A Quilt™ process map is then used to automatically determine the optimal process window to achieve the target CDs.
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- Award ID(s):
- 1951245
- PAR ID:
- 10224478
- Editor(s):
- Bannister, Julie; Mohanty, Nihar
- Date Published:
- Journal Name:
- Proc. SPIE 11615, Advanced Etch Technology and Process Integration for Nanopatterning X
- Volume:
- 11615
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1117/12.2583868
