The optical proximity correction using machine learning has been a promising alternative to physical three-dimensional Maxwell solvers in recent years. The benefits are mainly reduced CPU runtime and the incorporation of the resist and etching phenomena that lack proper physical models. The network architecture has been a key to the accuracy of the machine learning model. The appropriate architecture should grasp the physics and essential features in mask-resist mapping so that the test set prediction is improved. In addition, the architecture also affects the training process where the fine mask feature should be fitted and reflected in the corresponding resist patterns. In this work, we use a modified Unet with attention (Ozan Oktay et al. MIDL 2018) to construct a machine-learning model for OPC. The modification is on the attention layers inserted to the place where the up-sampling and cropped skip connection are combined. Instead of solely using concatenation to combine the up-sampled and skip-connected data flow, self-attention mechanism is shown to be effective in increasing the prediction accuracy. The mask-to-resist pattern, the image-to-image dataset, is from the Canon FPA
Machine learning (ML) based compact device modeling provides the opportunity for process-aware device modeling and thus process-aware circuit simulation. In contrast, incorporating semiconductor manufacturing parameters into compact models (CM) and subsequent circuit simulations using pure physics-based CM is difficult. We demonstrate process-aware circuit simulation where the effect of plasma treatment and thermal annealing can be directly reflected on the circuit output in SPICE transient simulation. The Verilog-A model input is V, frequency(f), area(A), and process conditions, i.e., plasma surface treatment (PST) and post-metal annealing (PMA). The MOSCAP capacitance-voltage (CV) characteristics under illumination are described by ML compact models.
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