Modeling post-lithography stochastic critical dimension variation with multi-task neural networks

A system for modeling distributions of post-lithography critical dimensions, comprising:

• a memory and at least one processor coupled to the memory, wherein the at least one processor is configured to:
• generate a plurality of aerial images of respective portions of a physical design layout of a semiconductor wafer;
• employ the plurality of aerial images as training data;
• generate a first portion of a neural network architecture, wherein the first portion comprises a neural network which is shared by a plurality of output channels;
• generate a second portion of the neural network architecture, wherein the second portion comprises a plurality of neural networks, each of the plurality of neural networks respectively corresponding to one of the plurality of output channels;
• train the first and second portions of the neural network architecture with the training data;
• and
• output the distributions of the post-lithography critical dimensions based on the plurality of output channels.

A computer program product for modeling distributions of post-lithography critical dimensions, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising:

• generating a plurality of aerial images of respective portions of a physical design layout of a semiconductor wafer;
• employing the plurality of aerial images as training data;
• generating a first portion of a neural network architecture, wherein the first portion comprises a neural network which is shared by a plurality of output channels;
• generating a second portion of the neural network architecture, wherein the second portion comprises a plurality of neural networks, each of the plurality of neural networks respectively corresponding to one of the plurality of output channels;
• training the first and second portions of the neural network architecture with the training data;
• and
• outputting the distributions of the post-lithography critical dimensions based on the plurality of output channels.

A method of modeling distributions of critical dimensions, comprising:

• receiving as inputs a plurality of aerial images of respective portions of a physical design layout of a semiconductor wafer;
• employing the plurality of aerial images as training data;
• generating a first neural network which is shared by at least three output channels;
• generating a plurality of second neural networks respectively corresponding to each one of the at least three output channels;
• training the first and second neural networks with the training data;
• and
• outputting the distributions of the critical dimensions based on the at least three output channels;
• wherein the method is performed by at least one computer system comprising at least one memory and at least one processor coupled to the memory.

A method of modeling distributions of critical dimensions, comprising:

• receiving as inputs a plurality of aerial images of respective portions of a physical design layout of a semiconductor wafer;
• employing the plurality of aerial images as training data;
• generating a first neural network which is shared by at least three parameters of the distributions of the critical dimensions, wherein the distributions are asymmetric;
• generating a plurality of second neural networks respectively corresponding to each one of the at least three parameters;
• training the first and second neural networks with the training data;
• and
• outputting the distributions of the critical dimensions based on the at least three parameters;
• wherein the method is performed by at least one computer system comprising at least one memory and at least one processor coupled to the memory.