A multi-category scanning acoustic image dataset: design, collection, and evaluation

Scanning Acoustic Microscopy (SAM) is an essential tool for the non-destructively deriving depth-specific information, which can detect the internal morphology and defect location of Integrated Circuit (IC) sensitively. When SAM equipment works, it will generate a large number of acoustic scanning images, which provide data support for defect detection algorithm. However, due to lack of professional researchers to uniformly sort and classify these acoustic scanning data and the deficiency of standard and available acoustic scanning image datasets, it is impossible to carry out the research on intelligent detection algorithm. In order to solve the above problems, a novel Multi-category Scanning Acoustic Image (MSAI) database is presented in this paper. MSAI database including 4565 acoustic scanning images acquired from 52 products in four packaging structures (BGA, QFN, SOIC and SOP). In order to prove the availability of MSAI database, four typical convolutional neural networks are used to identify and classify the MSAI database. The experimental results show that the VGG-16 model achieves the best classification performance in IC packaging structures grading, which show a train accuracy of 99.58% and a test accuracy of 99.44%, and all network models show a good inter-class separability on MSAI database.