This work presents a novel approach of using knowledge transfer to increase the accuracy of artificial neural network (ANN)-based device compact models, or neural compact models. This is useful when the amount of data available for training an ANN is limited. By utilizing relatively abundant data of a previous technology node, physical phenomena that are not evident in the limited data of the target technology node (e.g. gate-induced drain leakage) are accurately predicted. When meta learning algorithms are used, the accuracy of the model significantly increases, with relative linear error 10 times lower compared to the case when prior knowledge is not incorporated. The proposed methodology can be used to model future generation devices with limited data, utilizing data from well-characterized past technology node devices.

这项工作提出了一种使用知识转移来提高基于人工神经网络 (ANN) 的设备紧凑模型或神经紧凑模型的准确性的新方法。当可用于训练 ANN 的数据量有限时，这很有用。通过利用先前技术节点相对丰富的数据，准确预测在目标技术节点的有限数据中不明显的物理现象（例如栅极引起的漏极泄漏）。当使用元学习算法时，模型的准确性显着提高，相对线性误差比不包含先验知识的情况低 10 倍。所提出的方法可用于利用来自具有良好特征的过去技术节点设备的数据，对具有有限数据的下一代设备进行建模。