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초록

Traditionally, technology computer-aided design (TCAD) has been employed to analyze the effects of line edge roughness (LER). However, TCAD-based analysis is computationally prohibitive, particularly for circuit-level simulations like SRAM, which require extensive computational time. To efficiently analyze the impact of LER, this study proposes a hybrid model combining machine learning and an analytical model, offering high computational efficiency without compromising accuracy. Specifically, this study employs a convolutional neural network (CNN) that directly takes the surface roughness of fin-shaped field-effect transistors as input. This allows us to account for spatial characteristics, such as the correlation between two neighboring transistors, which is essential for predicting circuit characteristics. In this work, it first introduces the data generation process, the training process, and the CNN model with the optimized architecture. Next, it introduces a hybrid model that combines both a CNN and an analytical model to predict the voltage transfer characteristics curves and static noise margins for SRAM read and write operations. The proposed hybrid model demonstrates rapid learning and significant time savings in neural network preprocessing while maintaining high accuracy.

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