Compact semiconductor device models are essential for efficiently designing and analyzing large circuits. However, traditional compact model development requires a large amount of manual effort and can span many years. Moreover, inclusion of new physics (eg, radiation effects) into an existing compact model is not trivial and may require redevelopment from scratch. Machine Learning (ML) techniques have the potential to automate and significantly speed up the development of compact models. In addition, ML provides a range of modeling options that can be used to develop hierarchies of compact models tailored to specific circuit design stages. In this paper, we explore three such options: (1) table-based interpolation, (2)Generalized Moving Least-Squares, and (3) feed-forward Deep Neural Networks, to develop compact models for a p-n junction diode. We evaluate the performance of these "data-driven" compact models by (1) comparing their voltage-current characteristics against laboratory data, and (2) building a bridge rectifier circuit using these devices, predicting the circuit's behavior using SPICE-like circuit simulations, and then comparing these predictions against laboratory measurements of the same circuit.

中文翻译：

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用于电路仿真和分析的数据驱动紧凑型二极管模型的开发、演示和验证

紧凑型半导体器件模型对于高效设计和分析大型电路至关重要。然而，传统的紧凑模型开发需要大量的手动工作，并且可以跨越很多年。此外，将新物理（例如辐射效应）纳入现有的紧凑模型并非微不足道，可能需要从头开始重新开发。机器学习 (ML) 技术具有自动化并显着加快紧凑模型开发的潜力。此外，ML 提供了一系列建模选项，可用于开发针对特定电路设计阶段量身定制的紧凑模型层次结构。在本文中，我们探索了三个这样的选项：(1) 基于表格的插值，(2) 广义移动最小二乘法，和 (3) 前馈深度神经网络，以开发 pn 结二极管的紧凑模型。