The Experts below are selected from a list of 275148 Experts worldwide ranked by ideXlab platform
Gilles Rousselier - One of the best experts on this subject based on the ideXlab platform.
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Modeling of large strain multi-axial deformation of anisotropic metal sheets with strength-differential Effect using a Reduced Texture Methodology
International Journal of Plasticity, 2014Co-Authors: Gilles RousselierAbstract:This paper works on the macroscopic modeling of the anisotropic plasticity of a 6260-T6 thin-walled aluminum extrusion with a focus on the large strain multi-axial deformation with Strength-Differential Effect (SDE). Based on the framework of the self-consistent polycrystalline plasticity, the recently developed Reduced Texture Methodology (RTM) (Rousselier et al., 2012) is employed to provide the computational efficiency needed for industrial applications while keeping the physically-based nature of the plasticity model. In particular, the new model features a novel hardening law at slip-system level to better capture large strain behaviors, as well as a generic method designed to describe the stress/strain History Effect. All model parameters (including texture) are identified from mechanical experiments using a special optimization procedure. An extensive experimental program covering more than 30 distinct multi-axial stress states with both proportional and non-proportional loadings is used to calibrate and validate the present model. Both full- and reduced-thickness specimens are tested to capture the through-thickness heterogeneity of texture and grain size. It is shown that the present model predicts well the stress-strain responses in most of the multi-axial loading conditions which have been tested. Moreover, the model is able to capture various interesting behaviors of the present material during plastic deformation, including anisotropy, through-thickness heterogeneity, SDE of tension/compression or shear, and cross-hardening during non-proportional loadings. Furthermore, successful simulation of two structural level tests including a circular punch indentation and a three-point bending shows the applicability and potential of the new model in industrial practices.
Asif Islam Khan - One of the best experts on this subject based on the ideXlab platform.
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investigating ferroelectric minor loop dynamics and History Effect part i device characterization
IEEE Transactions on Electron Devices, 2020Co-Authors: Panni Wang, Zheng Wang, Xiaoyu Sun, Jae Hur, Suman Datta, Asif Islam KhanAbstract:Doped HfO2-based ferroelectric field-Effect transistor (FeFET) is being actively explored as an emerging nonvolatile memory device with the potential for in-memory computing. In this work, we identify a new challenge of ferroelectric partial switching, namely “History Effect” in minor loop dynamics. We develop a testing protocol to experimentally measure different transition paths in both ferroelectric capacitors (FeCap) and 28-nm high- ${k}$ metal-gate (HKMG) FeFET in Part I. The measurement results suggest that the intermediate states programming condition depends on the prior states that the device has gone through, and the condition may vary even when the transition occurs between the same starting and ending states. In Part II, a physics-based phase-field multidomain switching model is used to understand the origin of the History Effect.
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investigating ferroelectric minor loop dynamics and History Effect part ii physical modeling and impact on neural network training
IEEE Transactions on Electron Devices, 2020Co-Authors: Panni Wang, Zheng Wang, Xiaoyu Sun, Jae Hur, Suman Datta, Asif Islam KhanAbstract:Doped HfO2-based ferroelectric field-Effect transistor (FeFET) is being actively explored as an emerging nonvolatile memory device with the potential for in-memory computing. In this work, we identify a new challenge of ferroelectric partial switching, namely “History Effect” in minor loop dynamics. We experimentally demonstrate the minor loop dynamics in both ferroelectric capacitor (FeCap) and 28-nm FeFET in Part I. In this article, a physics-based phase-field multidomain switching model is used to understand the origin. Even though the device may have the same polarization state that is externally observable, its internal domain configuration varies depending on its History. We model such History Effect into the FeFET-based neural network simulation and analyze its negative impact on the training accuracy and then propose a possible mitigation strategy.
Panni Wang - One of the best experts on this subject based on the ideXlab platform.
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investigating ferroelectric minor loop dynamics and History Effect part i device characterization
IEEE Transactions on Electron Devices, 2020Co-Authors: Panni Wang, Zheng Wang, Xiaoyu Sun, Jae Hur, Suman Datta, Asif Islam KhanAbstract:Doped HfO2-based ferroelectric field-Effect transistor (FeFET) is being actively explored as an emerging nonvolatile memory device with the potential for in-memory computing. In this work, we identify a new challenge of ferroelectric partial switching, namely “History Effect” in minor loop dynamics. We develop a testing protocol to experimentally measure different transition paths in both ferroelectric capacitors (FeCap) and 28-nm high- ${k}$ metal-gate (HKMG) FeFET in Part I. The measurement results suggest that the intermediate states programming condition depends on the prior states that the device has gone through, and the condition may vary even when the transition occurs between the same starting and ending states. In Part II, a physics-based phase-field multidomain switching model is used to understand the origin of the History Effect.
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investigating ferroelectric minor loop dynamics and History Effect part ii physical modeling and impact on neural network training
IEEE Transactions on Electron Devices, 2020Co-Authors: Panni Wang, Zheng Wang, Xiaoyu Sun, Jae Hur, Suman Datta, Asif Islam KhanAbstract:Doped HfO2-based ferroelectric field-Effect transistor (FeFET) is being actively explored as an emerging nonvolatile memory device with the potential for in-memory computing. In this work, we identify a new challenge of ferroelectric partial switching, namely “History Effect” in minor loop dynamics. We experimentally demonstrate the minor loop dynamics in both ferroelectric capacitor (FeCap) and 28-nm FeFET in Part I. In this article, a physics-based phase-field multidomain switching model is used to understand the origin. Even though the device may have the same polarization state that is externally observable, its internal domain configuration varies depending on its History. We model such History Effect into the FeFET-based neural network simulation and analyze its negative impact on the training accuracy and then propose a possible mitigation strategy.
Suman Datta - One of the best experts on this subject based on the ideXlab platform.
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investigating ferroelectric minor loop dynamics and History Effect part i device characterization
IEEE Transactions on Electron Devices, 2020Co-Authors: Panni Wang, Zheng Wang, Xiaoyu Sun, Jae Hur, Suman Datta, Asif Islam KhanAbstract:Doped HfO2-based ferroelectric field-Effect transistor (FeFET) is being actively explored as an emerging nonvolatile memory device with the potential for in-memory computing. In this work, we identify a new challenge of ferroelectric partial switching, namely “History Effect” in minor loop dynamics. We develop a testing protocol to experimentally measure different transition paths in both ferroelectric capacitors (FeCap) and 28-nm high- ${k}$ metal-gate (HKMG) FeFET in Part I. The measurement results suggest that the intermediate states programming condition depends on the prior states that the device has gone through, and the condition may vary even when the transition occurs between the same starting and ending states. In Part II, a physics-based phase-field multidomain switching model is used to understand the origin of the History Effect.
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investigating ferroelectric minor loop dynamics and History Effect part ii physical modeling and impact on neural network training
IEEE Transactions on Electron Devices, 2020Co-Authors: Panni Wang, Zheng Wang, Xiaoyu Sun, Jae Hur, Suman Datta, Asif Islam KhanAbstract:Doped HfO2-based ferroelectric field-Effect transistor (FeFET) is being actively explored as an emerging nonvolatile memory device with the potential for in-memory computing. In this work, we identify a new challenge of ferroelectric partial switching, namely “History Effect” in minor loop dynamics. We experimentally demonstrate the minor loop dynamics in both ferroelectric capacitor (FeCap) and 28-nm FeFET in Part I. In this article, a physics-based phase-field multidomain switching model is used to understand the origin. Even though the device may have the same polarization state that is externally observable, its internal domain configuration varies depending on its History. We model such History Effect into the FeFET-based neural network simulation and analyze its negative impact on the training accuracy and then propose a possible mitigation strategy.
Jae Hur - One of the best experts on this subject based on the ideXlab platform.
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investigating ferroelectric minor loop dynamics and History Effect part i device characterization
IEEE Transactions on Electron Devices, 2020Co-Authors: Panni Wang, Zheng Wang, Xiaoyu Sun, Jae Hur, Suman Datta, Asif Islam KhanAbstract:Doped HfO2-based ferroelectric field-Effect transistor (FeFET) is being actively explored as an emerging nonvolatile memory device with the potential for in-memory computing. In this work, we identify a new challenge of ferroelectric partial switching, namely “History Effect” in minor loop dynamics. We develop a testing protocol to experimentally measure different transition paths in both ferroelectric capacitors (FeCap) and 28-nm high- ${k}$ metal-gate (HKMG) FeFET in Part I. The measurement results suggest that the intermediate states programming condition depends on the prior states that the device has gone through, and the condition may vary even when the transition occurs between the same starting and ending states. In Part II, a physics-based phase-field multidomain switching model is used to understand the origin of the History Effect.
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investigating ferroelectric minor loop dynamics and History Effect part ii physical modeling and impact on neural network training
IEEE Transactions on Electron Devices, 2020Co-Authors: Panni Wang, Zheng Wang, Xiaoyu Sun, Jae Hur, Suman Datta, Asif Islam KhanAbstract:Doped HfO2-based ferroelectric field-Effect transistor (FeFET) is being actively explored as an emerging nonvolatile memory device with the potential for in-memory computing. In this work, we identify a new challenge of ferroelectric partial switching, namely “History Effect” in minor loop dynamics. We experimentally demonstrate the minor loop dynamics in both ferroelectric capacitor (FeCap) and 28-nm FeFET in Part I. In this article, a physics-based phase-field multidomain switching model is used to understand the origin. Even though the device may have the same polarization state that is externally observable, its internal domain configuration varies depending on its History. We model such History Effect into the FeFET-based neural network simulation and analyze its negative impact on the training accuracy and then propose a possible mitigation strategy.