A method is presented for constructing a deep neural network based model to concurrently simulate post-lithography critical dimensions (CDs) and post-etch critical dimensions (CDs) and to improve the modeling accuracy of each process respectively. The method includes 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.