Neural network-based error compensation method for mass transfer of mini-light-emitting diode (mini-led) chips

A neural network-based error compensation method for mass transfer of mini-light-emitting diode (Mini-LED) chips, comprising:

• (S1) obtaining automated optical re-inspection results as first results, wherein the first results comprise data variables of each of the Mini-LED chips, and the data variables comprise a transfer status identifier, a first X-direction transfer error and a first Y-direction transfer error;
• (S2) removing irrelevant variables from the data variables;
• and sorting the first results according to a transfer sequence followed by normalization to obtain a second result;
• (S3) performing nearest-neighbor interpolation on a Mini-LED chip with a transfer status identifier being abnormal in the second result;
• according to an X-direction transfer error and a Y-direction transfer error in the second result, calculating a chip distance and a chip angular position in a polar coordinate system followed by removal of outliers;
• according to the chip angular position on a wafer, calculating a chip angle information, and converting the chip angle information into a rectangular coordinate system information to calculate a differential of a path variable of each of the Mini-LED chips;
• (S4) defining a multi-layer neural network model and a dropout ratio corresponding thereto;
• constructing a loss function based on the chip distance in the polar coordinate system and a predicted value thereof, and the chip angular position in the polar coordinate system and a predicted value thereof;
• updating a weight of the multi-layer neural network model using an optimizer and the loss function until no overfitting is observed after the chip distance in the polar coordinate system, the chip angular position in the polar coordinate system, the differential of the path variable and the second result in a test set are input into the multi-layer neural network model;
• and
• (S5) generating a chip transfer path;
• inputting the chip transfer path into the multi-layer neural network model to obtain a predicted transfer error value;
• and performing mass transfer of the Mini-LED chips based on the predicted transfer error value.