Semiconductor Engineering

Benefits And Limits Of Using ML For Materials Discovery

Machine learning tools can accelerate all stages of materials discovery, from initial screening to process development.
Edutopia

Simple Swaps for Moving From Passive to Active Learning

These tips demonstrate that planning for active learning experiences doesn’t have to be time-intensive or difficult.
TechAnnouncer

Transforming Learning: Engaging ‘Technology in the Classroom’ Video Examples

Using video can really help students understand things better, especially when the videos are like explainer videos they can ...

From early cars to generative AI, new technologies create demand for specialized materials

Generative artificial intelligence has become widely accepted as a tool that increases productivity. Yet the technology is ...
Devdiscourse

AI-driven prompts can turn static classroom materials into personalized learning tools

The authors note that large language models, particularly generative systems capable of producing contextualized text, are ...
IEEE

On Code Example-Aided Just-In-Time Learning for Programming Education

Abstract: Context: Programming education keeps facing chal-lenges. A significant challenge is the mismatch between the increasing student demand and the shortage of teaching workforce on personal ...
IEEE

Physics-Informed Multi-Task Learning for Material Removal Rate Prediction in Semiconductor Chemical Mechanical Planarization

Abstract: The chemical mechanical planarization (CMP) process is a complex and critical operation in the semiconductor manufacturing industry, involving a wide variety of process parameters. As a ...