Inverse design system designing nano-optical device using controllable generative adversarial network and training and design methods

A method of training an inverse design system using a controllable Generative Adversarial Network (cGAN), wherein the inverse design system is driven by a computer system, receives a target characteristic, and generates a structure pattern for a nano-optical device, the method comprising:

• training a discriminator and a classifier of the cGAN using simulation data associated with the nano-optical device;
• generating a fake image of the structure pattern using a generator of the cGAN by combining the target characteristic and random noise;
• calculating a discrimination error used to determine whether the fake image is real or fake by providing the fake image to the discriminator;
• determining a characteristic classification error for the fake image by providing the fake image to the classifier;
• and
• competitively training the generator, the discriminator, and the classifier with reference to the discrimination error and the characteristic classification error.

An inverse design method for designing a dielectric pattern of an image sensor using a controllable Generative Adversarial Network (cGAN), the method comprising:

• collecting simulation data related to transmittance for each wavelength among a plurality of wavelengths relevant to the dielectric pattern;
• training a generation model based on the cGAN driven in a computing system using the simulation data to provide a trained generation model;
• generating a dielectric pattern image by inputting a maximum transmittance wavelength to the trained generation model;
• and
• designing the image sensor including the dielectric pattern in accordance with the dielectric pattern image.