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Assembly Line

Fabs Drive Deeper Into Machine Learning

Date:

Author: Anne Meixner

Topics: machine learning, machine vision, defect detection, convolutional neural network

Vertical: Semiconductor

Organizations: GlobalFoundries, KLA, SkyWater Technology, Onto Innovation, CyberOptics, Hitachi, Synopsys

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.

Read more at Semiconductor Engineering