Onto Innovation
Assembly Line
Onto Innovation Debuts Sub-surface Defect Inspection for Advanced Packaging
Onto Innovation Inc. announced the release of a new sub-surface inspection capability for the Dragonfly® G3 sub-micron 2D/3D inspection and metrology platform. The new capability enables whole wafer inspection for critical yield impacting defects that can lead to lost die as well as entire wafers breaking in subsequent process steps. Such defects were previously impossible to find in a production environment. In today’s world of wafer thinning and multi-layer wafer or die bonding, sub-surface defects are far more dangerous than ever before as bonded layers are now a tenth of their former thickness and far more brittle and therefore more susceptible to damage pre- or post-bonding. Sub-surface defects that occur during the bonding or thinning process such as micro-cracks can cause not only die yield issues, but wafers can be shattered resulting in the loss of hundreds of die in an instant.
Onto Innovation’s 4Di InSpec™ Automated Metrology System Receives 2024 Innovative System of the Year Award from FANUC America
Onto Innovation Inc. and its Tucson subsidiary 4D Technology today announced they’ve been named winner of FANUC America’s prestigious 2024 Innovative System of the Year award for the 4Di InSpec automated metrology system (AMS). The system enables automated surface defect and feature metrology for aviation, aerospace and other applications in the industrial manufacturing market. The patented, vibration-immune technology enables the unique capability of using non-contact, three-dimensional optical metrology on the production floor, providing new levels of defect inspection with micrometer-level resolution. In partnership with OptiPro Systems, the 4Di InSpec AMS systems were delivered in the second half of 2023 to several leading aerospace engine manufacturers.
Fabs Drive Deeper Into Machine Learning
For the past couple decades, semiconductor manufacturers have relied on computer vision, which is one of the earliest applications of machine learning in semiconductor manufacturing. Referred to as Automated Optical Inspection (AOI), these systems use signal processing algorithms to identify macro and micro physical deformations.
Defect detection provides a feedback loop for fab processing steps. Wafer test results produce bin maps (good or bad die), which also can be analyzed as images. Their data granularity is significantly larger than the pixelated data from an optical inspection tool. Yet test results from wafer maps can match the splatters generated during lithography and scratches produced from handling that AOI systems can miss. Thus, wafer test maps give useful feedback to the fab.