M. M. Manjurul Islam

Abstract Automated identification of wafer defect maps (WDMs) is critical for yield enhancement and quality control in semiconductor manufacturing. However, as defect patterns become increasingly diverse and dynamic, conventional supervised methods remain constrained by their dependence on fully labelled data and their limited adaptability to changing defect distributions.

Abstract Deep neural networks in medical and edge environments often face computational and memory constraints, which necessitate effective model compression. Neural network pruning is a widely used solution, but conventional methods often rely on magnitude-based or random criteria that can remove entire functional groups of neurons and reduce model performance.