The invention relates to the technical field of
machine learning, and discloses a
ceramic chip packaging process parameter optimization method based on
machine learning, and the method comprises the steps: collecting first historical data of a target
ceramic chip in a packaging process, and carrying out the preprocessing of the first historical data, and extracting second historical data; constructing a
chip stress prediction model according to the second historical data; after real-
time data is collected and preprocessed, the
stress level of the chip in the future time period is predicted by combining the prediction model; if the predicted
stress level is a warning or danger level, dynamically adjusting packaging process parameters of the
ceramic chip; according to the method, the future stress
risk level of the chip is predicted through a
machine learning method, and the packaging process parameters are dynamically optimized and adjusted according to the prediction result, so that the
stress failure risk in the packaging process of the ceramic chip is effectively reduced, and the reliability and yield of the chip are remarkably improved.