ScholarWorks@성균관대학교

초록

Large-scale simulations of semiconductor devices are crucial for understanding their behavior, yet each iteration of these simulations can take hours or even days. The complexity of modern transistors intensifies this challenge, demanding even more simulations to capture their diverse characteristics accurately. On the other hand, with the emergence of foundation models and other learning-based methods in semiconductors, it becomes critical to develop a systematic methodology for curating abundant, high-quality device data. In response to these challenges, we propose FuncFlow, a generative neural operator for augmenting device simulations. FuncFlow learns the probability distribution of arbitrary device types and generates valid simulation results at the corresponding technology scale within seconds. We evaluate FuncFlow over capacitance-voltage (CV) curve generations at different technology scales and demonstrate that FuncFlow can successfully learn the distribution and generate physically valid CV curves at each scale, significantly reducing the simulation time.

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