An optimized source has the ability to improve the process window during lithography in semiconductor manufacturing. Source optimization is always a key technique to improve printing performance. Conventionally, source optimization relies on mathematical–physical model calibration, which is computationally expensive and extremely time-consuming. Machine learning could learn from existing data, construct a prediction model, and speed up the whole process. We propose the first source optimization process based on autoencoder neural networks. The goal of this autoencoder-based process is to increase the speed of the source optimization process with high-quality imaging results. We also make additional technical efforts to improve the performance of our work, including data augmentation and batch normalization. Experimental results demonstrate that our autoencoder-based source optimization achieves about 10⁵  ×   speed up with 4.67% compromise on depth of focus (DOF), when compared to conventional model-based source optimization method.