Ultrasonic Wire Bond Outlier Classification

Abstract

K&S developed and tested the Advanced Process Diagnostics (APD) algorithm to classify bonding outliers in ultrasonic wire bond production. APD is a software feature, part of Kulicke & Soffa wedge bonders to measure and analyze process signals and detect and classify bond outliers. APD helps bonder operators, production supervisors and process engineers to detect process deviations and fix the underlying root causes. APD measures bond signals, such as deformation, ultrasonic current and ultrasonic frequency. Bonds are automatically divided into subgroups based on bond order and process parameters and the signals within a subgroup are then normalized. For outlier classification, the features are extracted from the normalized signals and combined into failure class values. The failure classes, such as contamination, misaligned wire and unstable substrate, are calculated independently. Within the APD feature, a user can define limits for the failure class values and define bonder actions based on the severity of the detected outlier. We measured the detection rates for large wire Al bond failure classes and demonstrate how APD calculates failure class values from the signals. Additionally, we show how new signal features and failure classes can be defined to detect production specific or rare failure classes. Finally, we present outlier classification performance metrics against large production data sets.