Application of Two Hopfield Neural Networks for Automatic Four-Element LED Inspection

Abstract

A system for the automatic inspection of LED wafer defects is proposed to detect defective dies in a four-element (aluminum gallium indium phosphide, AlGaInP) wafer. There are over 80000 dies on an LED wafer. Defective dies are typically visually identified with the aid of a scanning electron microscope. This process involves dozens of operators or engineers visually checking the wafers and hand marking the defective dies. However, wafers may not be fully and thoughtfully checked, and different observers usually find different results. These shortcomings lead to significant labor and production costs. Therefore, a solution that consists of two Hopfield neural networks, of which one is used to identify the LED die regions and the other is used to cluster the die into three groups, is proposed to facilitate the detection of defective dies in wafer images. The experimental results show that the proposed method successfully detects defective dies in a four-element wafer.