| dc.description.abstract | With the vigorous development of artificial intelligence, network technology and the demand for 5G RF technology modules, the LTCC low-temperature co-fired ceramic substrate material of hard materials has been pushed to a historical high. Under the requirement of miniaturization, heat dissipation and processing accuracy are highly valued; the hardness of ceramics after sintering is second only to diamonds, so the cost of post-processing is high, and the green embryos before sintering are like chalk and dough, so ceramic processing is usually two-stage. , Rough machining with a large amount of raw embryos and precision machining after sintering; however, the shrinkage after sintering causes great uncertainty, so that the processing accuracy before sintering becomes difficult to control after sintering, plus the interlayer metal circuit The effect causes uneven shrinkage, which is a test of dimensional accuracy.
This paper proposes a precision control method from green embryo processing to sinter shrinkage in the LTCC process, integrating automatic optical measurement and AI automatic positioning identification, quickly grasping QC results, and timely correcting processing parameters. Through deep learning principles, the following goals can be achieved:
(1)The size shrinkage error before and after sintering can be quickly compared in the same batch of materials, the parameter setting of green embryo processing can be corrected in time, and the concept of timely compensation can reduce the center point deviation of the final target size.
(2)From the method of eliminating defective products after completion, to the predicted tolerance range, the suspension signal is raised before the tolerance is exceeded, which can improve the yield to more than 4~5% and approach the 100% target.
(3)For any two related stations, the measurement information of each back station is immediately returned to the previous station, through the depth Learning, in addition to real-time optimization accuracy, a complete database has been established. The more production samples, the higher the reliability. It provides good tools for product design optimization, incoming inspection or process improvement. | en_US |