Solvent Vapor Annealing, Defect Analysis, and Optimization of Self-Assembly of Block Copolymers Using Machine Learning Approaches

Keyphrases

• Block Copolymer Self-assembly 100%
• Machine Learning Approach 100%
• Solvent Vapor Annealing 100%
• Defect Analysis 100%
• Block Copolymer 80%
• Defectivity 60%
• Small Variation 40%
• Film Thickness 40%
• Self-assembly 40%
• Design of Experiments 40%
• Flow Control System 40%
• Machine Design 40%
• Pattern-based Approach 20%
• Machine Learning 20%
• Microelectronics 20%
• High-resolution 20%
• Polystyrene 20%
• Double Layer 20%
• Annealing Conditions 20%
• Wetting Layer 20%
• Nanopatterning 20%
• Microfabrication 20%
• Parameter Space 20%
• Narrow Region 20%
• Interaction Parameter 20%
• Dimethylsilane 20%
• Processing Factor 20%
• Experimental Parameters 20%
• Swelling Degree 20%
• Vapor Temperature 20%
• Nanostructured Surface 20%
• Processing Parameters 20%
• Grain Coarsening 20%
• Solvent Vapor Pressure 20%
• Density multiplication 20%
• Dot Array 20%

Engineering

• Learning Approach 100%
• Defectivity 100%
• Defects 100%
• Design of Experiments 66%
• Microfabrication 33%
• Microelectronics 33%
• Optimum Condition 33%
• Double Layer 33%
• Length Scale 33%
• Experimental Parameter 33%
• Macroscale 33%
• Processing Parameter 33%
• Interaction Parameter 33%
• Patterning Technique 33%
• Grain Coarsening 33%
• High Resolution 33%
• Parameter Space 33%

Material Science

• Block Copolymer 100%
• Self Assembly 100%
• Film Thickness 33%
• Coarsening 16%
• Microfabrication 16%
• Microelectronics 16%
• Polystyrene 16%
• Density 16%