In model compression, filter pruning stands out as a pivotal technique. Its significance becomes particularly crucial as the present deep learning models are developed into larger and more complicated architectures, which leads to massive parameters and high floating-point operations per second (FLOPs). Challenges have appeared due to the high computational demands associated with these advanced model structures. In this work, we introduce two novel methods aimed at addressing the challenges above: innovative automatic filter pruning methods via semi-supervised multi-task learning (SSMTL) hypernetwork and partial weight training hypernetwork, respectively. Both methods effectively train the hypernetwork and enhance the precision of the neural architecture search with reinforcement learning. Compared to other filter pruning methods, our approach achieves higher model accuracy at similar pruning ratios.
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.
Improving spectral photon harvesting is important for thin-film multijunction cells. We show that efficient spectral flux management can be achieved using genetic algorithm-optimized surface plasmon (SP) cavity-resonant type multijunction cells. We also observe that the excitation of the SP quasi-guided mode, Fabry–Perot mode, and SP polariton significantly enhance the photocurrent of multijunction cells. Two types of cavity structures are investigated. For the optimized SP intermediate reflector and bottom-grating cavity, the resonant cavity mode efficiently increases the long-wavelength absorption in the bottom cell by 63.27%, resulting in reduced absorbance asymmetry between the top and the bottom cells. Accordingly, the matched integrated absorbance is increased by 14.92%. For the optimized SP top- and bottom-grating (TBG) cavity, the integrated absorbance and current matching are improved due to the higher transmission through the solar cell front surface and the excitation of the quasi-guided mode with a more localized field in the bottom cell. The matched integrated absorbance is improved by 85.68% for the TBG cavity.
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