A novel motherboard test item yield prediction model based on parallel feature extraction
Year: 2024
Type: conference-paper
Abstract: Functional testing of motherboards in Surface Mount Technology (SMT) assembly lines is crucial. Accurate yield prediction for each test item optimizes testing strategies, reduces costs, and ensures test coverage. Manual estimation of test item yields remains common, hindering accurate on-site predictions. Existing research on motherboard yield lacks predictions for individual test items and ignores temporal correlations during placement. This paper introduces a method, a convolutional bidirectional long short-term memory attention network (CBA-Net), which combines a convolutional neural network and a bidirectional long short-term memory network with an attention mechanism for parallel processing. It preprocesses historical test data, leveraging both networks to identify key features and extract temporal correlations. The attention mechanism optimizes yield predictions by assigning weights to information at different time steps. Experimental validation using actual production data demonstrates that the proposed method performs better compared to traditional models.
Authors: Zhangpeng Yan, Weimin Zhai, Chao Li
Institution: University of Science and Technology of China
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4401518756