Numerical Simulation and Experimental Validation of Nanoindentation of Silicon Using Finite Element Method

Barkachary, Borad M.; Joshi, Shrikrishna N.

doi:10.1007/978-981-32-9072-3_72

Cited by 1 publication

(1 citation statement)

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“…In these FE models, thin films are assumed as deformable semi-infinite media, and the maximum indenter depth is kept shallow regarding the total film thickness. However, seeking simplicity, the indenter is assumed as a rigid body [38,39]. This assumption simplifies the computational model by negating the need to account for indenter deformation.…”

Section: Finite Element Modelingmentioning

confidence: 99%

Identification of Elastoplastic Constitutive Model of GaN Thin Films Using Instrumented Nanoindentation and Machine Learning Technique

Khalfallah,

Benzarti

2024

Coatings

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This study presents a novel inverse identification approach to determine the elastoplastic parameters of a 2 µm thick GaN semiconductor thin film deposited on a sapphire substrate. This approach combines instrumented nanoindentation with finite element (FE) simulations and an artificial neural network (ANN) model. Experimental load–depth curves were obtained using a Berkovich indenter. To generate a comprehensive database for the inverse analysis, FE models were constructed to simulate load–depth responses across a wide range of GaN thin film properties. The accuracy of both 2D and 3D simulations was compared to select the optimal model for database generation. The Box–Behnken design-based data sampling method was used to define the number of simulations and input variables for the FE models. The ANN technique was then employed to establish the complex mapping between the simulated load–depth curves (input) and the corresponding stress–strain curve (output). The generated database was used to train and test the ANN model. Then, the learned ANN model was used to achieve high accuracy in identifying the stress–strain curve of the GaN thin film from the experimental load–depth data. This work demonstrates the successful application of an inverse analysis framework, combining experimental nanoindentation tests, FE modeling, and an ANN model, for the characterization of the elastoplastic behavior of GaN thin films.

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Section: Finite Element Modelingmentioning

confidence: 99%