The invention discloses a
wafer quality
analysis method based on image recognition, and particularly relates to the field of
semiconductor manufacturing quality detection, which comprises the following steps: synchronously acquiring
wafer two-dimensional images under visible light,
infrared and
ultraviolet spectrums through a multispectral and high-resolution fusion camera combination model; the
structured light three-dimensional reconstruction technology is used for obtaining surface three-dimensional shape data, a self-adaptive
noise suppression
algorithm is applied,
noise features are recognized through
frequency domain analysis, filtering parameters are dynamically adjusted, targeted
noise reduction is achieved, a multi-scale feature
pyramid and
quantum dot mark recognition fusion model is constructed, defect contours and material heterogeneous areas are extracted in a layered mode, and the defect detection precision is improved. The method comprises the following steps: extracting
microstructure characteristic parameters by combining a
quantum dot
fluorescence labeling technology, mapping microscopic characteristics and three-dimensional shape data to a multi-
modal space model, generating three-property parameters of a
wafer, establishing a three-dimensional
evaluation system, comprehensively judging a quality grade and outputting an analysis report, so that the precision and efficiency of wafer quality detection are remarkably improved.