Method and device for optical system online designing based on intelligent light computing, and storage medium

An electronic device, comprising:

• a processor;
• and
• a memory storing instructions executable by the processor,
• wherein the processor is configured to:
• construct an optical system based on a differentiable online neural network and map light propagation input data to the differentiable online neural network to obtain a network mapping result;
• train the differentiable online neural network based on the network mapping result and a fully-forward-mode, to compute first light propagation output data of the optical system;
• compute an error field according to the first light propagation output data and application target data, and input the error field to the optical system in the fully-forward-mode to output second light propagation output data;
• and
• compute gradient information of an online neural network parameter based on the second light propagation output data, and update the online neural network parameter according to the gradient information to obtain a trained optical system parameter, such that the trained optical system outputs data corresponding to the application target data to enable self-designing of the optical system.

A non-transitory computer-readable storage medium having stored thereon instructions that, when executed by a processor of an electronic device, cause the electronic device to perform a method for optical system online designing based on intelligent optical computing, the method comprising:

• constructing an optical system based on a differentiable online neural network and mapping light propagation input data to the differentiable online neural network to obtain a network mapping result;
• training the differentiable online neural network based on the network mapping result and a fully-forward-mode, to compute first light propagation output data of the optical system;
• computing an error field according to the first light propagation output data and application target data, and inputting the error field to the optical system in the fully-forward-mode to output second light propagation output data;
• and
• computing gradient information of an online neural network parameter based on the second light propagation output data, and updating the online neural network parameter according to the gradient information to obtain a trained optical system parameter, such that the trained optical system outputs data corresponding to the application target data to enable self-designing of the optical system.