Co-modeling post-lithography critical dimensions and post-etch critical dimensions with multi-task neural networks

A method for constructing a deep neural network based model to concurrently simulate post-lithography critical dimensions (CDs) and post-etch critical dimensions (CDs), the method comprising:

• generating lithographic aerial images of physical design layout patterns;
• constructing a multi-task neural network including two output channels;
• training the multi-task neural network with the training data of the lithographic aerial images;
• and
• outputting simulated critical dimension values pertaining to lithography and etch processes.

A system configured to train a machine learning based model, the system comprising:

• one or more computer subsystems;
• and
• one or more components executed by the one or more computer subsystems, wherein the one or more components include the machine learning based model configured for performing one or more simulations by:
• generating lithographic aerial images of selected physical design layout patterns, the lithographic aerial images employed as training data;
• constructing a first portion of a neural network to be shared by two output channels;
• constructing a second portion of the neural network to be customized for each of the two output channels;
• training the neural network with the training data;
• and
• outputting simulated critical dimension values for lithography and etch processes.