The present application belongs to the technical field of photonic device design, and specifically relates to a micro-ring
resonator reverse design optimization method based on
deep learning. The present application comprises: obtaining different micro-ring
resonator structure parameters and corresponding
free spectral range and quality factor, and
processing and dividing the
data set; building a
cascade neural
network model, wherein the reverse network takes the target
performance index as the input to predict the structure parameter, the forward network inputs the structure parameter and outputs the
performance index, and is used to constrain and correct the reverse prediction result; after training, the
test set is evaluated to measure the
reverse effect by the average absolute percentage error index, the weight in the
loss function is changed, and multiple rounds of training are performed to determine the
optimal weight configuration and final model of the reverse design. The present application has the advantages of high calculation efficiency, excellent design precision and strong physical
realizability, can reduce the dependence on large-scale
simulation and multiple iteration optimization, effectively shorten the device
design cycle, and has high application value.