The Performance Advancement of Test Algorithm Using Neural Network for Semiconductor Packages

Jae Yeol Kim; Jae Ki Sim; M.J. Song; Chang Hoi Kim; Lee Ku Kwac

doi:10.4028/www.scientific.net/KEM.261-263.411

Paper Titles

Effect of the Groove Shape of Ultra Thick Box-Column with Center Segregation under High Heat Input for Corner Welding
p.387

Mode III Interfacial Edge Crack in a Magnetoelectroelastic Bimaterial
p.393

Parametric Study of Combined Adhesive-Riveted Lap Joints
p.399

Effects of Surface Treatment and Loading Speed on Adhesive Strength of Aluminum to Polycarbonate Lap Joints
p.405

The Performance Advancement of Test Algorithm Using Neural Network for Semiconductor Packages
p.411

Measurement of Mechanical Properties of Electroplated Nickel Thin Film
p.417

Properties of WC-Co Ceramic Coatings of Ultra-Fine Grains Prepared by Electro-Thermal Explosion Method
p.423

Fracture Strength of WC-12Co Thermal Sprayed Coating under Mixed Loading Mode I&III in Crack Surface Displacement
p.429

Coating Interface Fracture Toughness Evaluation by a Combination of Edge Compression and Slinging Load
p.435

HomeKey Engineering MaterialsKey Engineering Materials Vols. 261-263The Performance Advancement of Test Algorithm...

The Performance Advancement of Test Algorithm Using Neural Network for Semiconductor Packages

Abstract:

Availability of defect test algorithm that recognizes exact and standardized defect information in order to fundamentally resolve generated defects in industrial sites by giving artificial intelligence to SAT(Scanning Acoustic Tomograph), which previously depended on operator’s decision, to find various defect information in a semiconductor package, to decide defect pattern, to reduce personal errors and then to standardize the test process was verified. In order to apply the algorithm to the lately emerging Neural Network theory, various weights were used to derive results for performance advancement plans of the defect test algorithm that promises excellent field applicability.