One step automated unpatterned wafer defect detection and classification

Abstract

Automated detection and classification of crystalline defects on micro-grade silicon wafers is extremely important for integrated circuit (IC) device yield. High training cost, limited capability of classifying defects, increasing possibility of contamination, and unexpected human mistakes necessitate the need to replace the human visual inspection with automated defect inspection. The Laser Scanning Surface Inspection Systems (SSISs) equipped with the Reconvergent Specular Detection (RSD) apparatus are widely used for final wafer inspection. RSD, more commonly known as light channel detection (LC), is capable of detecting and classifying material defects by analyzing information from two independent phenomena, light scattering and reflecting. This paper presents a new technique including a new type of light channel detector to detect and classify wafer surface defects such as slipline dislocation, Epi spikes, Pits, and dimples. The optical system to study this technique consists of a particle scanner to detect and quantify light scattering events from contaminants on the wafer surface and a RSD apparatus (silicon photo detector). Compared with the light channel detector presently used in the wafer fabs, this new light channel technique provides higher sensitivity for small defect detection and more defect scattering signatures for defect classification. Epi protrusions (mounds and spikes), slip dislocations, voids, dimples, and some other common defect features and contamination on silicon wafers are studied using this equipment. The results are compared quantitatively with that of human visual inspection and confirmed by microscope or AFM. This new light channel technology could provide the real future solution to the wafer manufacturing industry for fully automated wafer inspection and defect characterization.