The invention discloses an optimal
feature vector design method for carrying out reverse photolysis based on
machine learning. The method comprises the following steps: dividing a design target pattern into a plurality of grid units; calculating a characteristic function set {Ki (x, y)} according to imaging conditions, i = 1, 2,... N1; establishing a neural
network model, and selecting a trainingsample and a
verification sample which need to be included in training; calculating a
signal set {Si (x, y)} of each grid unit by using a characteristic function set {Ki (x, y)}; taking the value of the strict reverse photoetching at the corresponding position as a target value of neural network training; training, different input end dimensions N1, the number N2 of hidden
layers and the number M1of neurons of each
hidden layer are adopted; training the neural
network model according to different combinations of the input end dimension N1, the
hidden layer number N2 and the
neuron number M1,M2,... MN2 by using a training sample, and verifying the neural
network model by using a
verification sample until the neural network model with satisfactory combinations of the input end dimension N1, the
hidden layer number N2 and the
neuron number M1, M2,... MN2 of each hidden layer is found. Therefore, according to the design method, the neural network does not need a
feature extraction layerany more, so that the
network architecture is simplified, and the
training time is shortened.