The invention provides a DDR
wafer test
anomaly detection method and device based on
machine learning, and the method comprises the steps: obtaining a
time sequence eye pattern synchronously collected in a DDR
wafer test process and
time sequence waveform data related to total
jitter, deterministic
jitter and random
jitter, and carrying out the
clock edge phase segmentation alignment of differential
time sequence sampling points in the time sequence waveform data,
phase domain dense sampling data is constructed, adaptive sparse coding is completed in combination with DDR jitter physical priori,
noise and normal process fluctuation related components are removed, defect sensitive sparse components are obtained,
variational mode decomposition is performed, components related to process drift, normal process fluctuation and abrupt change abnormity are obtained, and the defect sensitive sparse components are subjected to sparse coding; and the abrupt change anomaly correlation component is extracted as target detection data, a preset
machine learning model is input, an anomaly mode correlation result is generated, a DDR
wafer test
anomaly detection result is output based on the result, and a corresponding test setting item adjustment instruction is generated. According to the invention, the
overall efficiency and the result reliability of the DDR wafer test can be improved.