Search Results - "lithograph"

Physics of Quantum Rings

Table of Contents: “…Quantum Ring: A Unique Playground for the Quantum-mechanical Paradigm -- Optical Berry Phase in Micro/nano-rings -- From Dot to Ring: Tunable Exciton Topology on Type II InAs/GaAsSb Quantum Nanostructures -- Self-organized Quantum Rings: Physical Characterization and Theoretical Modeling -- Scanning-probe Electronic Imaging of Lithographically Patterned Quantum Rings -- Functionalization of Droplet Etching for Quantum Rings -- Fabrication of Ordered Quantum Rings by Molecular Beam Epitaxy -- Self-assembled Semiconductor Quantum Rings Complexes by Droplet Epitaxy: Growth and Physical Properties -- Optical Aharonov-Bohm Oscillations of an Exciton and a Biexciton in a Quantum Ring -- Aharonov-Bohm Effect for Neutral Excitons in Quantum Rings -- Electronic, Magnetic and Optical Properties of Quantum Rings of Novel Materials -- Spin Interference Effects in Rashba Rings -- Quantum Rings in Electromagnetic Fields -- Intense THz Radiation Effect on Electronic and Intraband Optical Properties of Semiconductor Quantum Rings -- Electron-phonon Interaction in Ring-like Nanostructures -- Differential Geometry Applied to Rings and Möbius Nanostructures -- Band Mixing Effects in InAs/GaAs Quantum Rings and in the Ring-like Behaving MoS2 Quantum Dots -- Circular n-p Nanojunctions in Graphene Nanoribbons.…”
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Machine Learning in VLSI Computer-Aided Design

Table of Contents: “…Chapter1: A Preliminary Taxonomy for Machine Learning in VLSI CAD -- Chapter2: Machine Learning for Compact Lithographic Process Models -- Chapter3: Machine Learning for Mask Synthesis -- Chapter4: Machine Learning in Physical Verification, Mask Synthesis, and Physical Design -- Chapter5: Gaussian Process-Based Wafer-Level Correlation Modeling and its Applications -- Chapter6: Machine Learning Approaches for IC Manufacturing Yield Enhancement -- Chapter7: Efficient Process Variation Characterization by Virtual Probe -- Chapter8: Machine learning for VLSI chip testing and semiconductor manufacturing process monitoring and improvement -- Chapter9: Machine Learning based Aging Analysis -- Chapter10: Extreme Statistics in Memories -- Chapter11: Fast Statistical Analysis Using Machine Learning -- Chapter12: Fast Statistical Analysis of Rare Circuit Failure Events -- Chapter13: Learning from Limited Data in VLSI CAD -- Chapter14: Large-Scale Circuit Performance Modeling by Bayesian Model Fusion -- Chapter15: Sparse Relevance Kernel Machine Based Performance Dependency Analysis of Analog and Mixed-Signal Circuits -- Chapter16: SiLVR: Projection Pursuit for Response Surface Modeling -- Chapter17: Machine Learning based System Optimization and Uncertainty Quantification of Integrated Systems -- Chapter18: SynTunSys: A Synthesis Parameter Autotuning System for Optimizing High-Performance Processors -- Chapter19: Multicore Power and Thermal Proxies Using Least-Angle -- Chapter20: A Comparative Study of Assertion Mining Algorithms in GoldMine -- Chapter21: Energy-Efficient Design of Advanced Machine Learning Hardware.…”
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Solid Modeling and Applications Rapid Prototyping, CAD and CAE Theory / by Um, Dugan

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