通过无导数优化进行紧凑模型参数提取
Compact Model Parameter Extraction via Derivative-Free Optimization
AI总结：
本文提出一种基于无导数优化的紧凑模型参数提取方法，通过改进损失函数和训练-测试分割技术，实现了对金刚石肖特基二极管和GaN-on-SiC HEMT的高效、鲁棒建模，显著降低了传统人工提取的时间成本并有效应对异常值。
摘要：
在本文中，我们解决了紧凑模型参数提取问题，通过无导数优化同时提取数十个参数。传统上，参数提取是通过将完整参数集划分为较小的子集来手动执行的，每个子集针对器件的不同工作区域，这一过程可能需要数天或数周的时间。我们的方法通过采用无导数优化来简化这一过程，以识别最适合紧凑模型的良好参数集，而无需执行大量模拟。我们进一步增强了优化过程，以解决器件建模中的三个关键问题，方法是仔细选择一个损失函数，该函数关注相对误差而非绝对误差，以确保在不同数量级上保持一致的性能，优先考虑特定阈值以上关键工作区域的准确性，并降低对异常值的敏感性。此外，我们利用训练-测试分割的概念来评估模型拟合并避免过拟合。我们通过成功建模金刚石肖特基二极管（使用SPICE二极管模型）和GaN-on-SiC HEMT（使用ASM-HEMT模型）展示了我们方法的有效性。对于后者，涉及提取ASM-HEMT DC模型的35个参数，我们在不到6,000次试验中确定了最佳参数集。还提供了使用这两种器件的额外示例以证明对异常值的鲁棒性，表明即使故意损坏超过25%的数据，也能实现出色的拟合。这些示例证明了我们方法的实用性，突出了无导数优化在器件建模中的优势。
英文摘要：
In this paper, we address the problem of compact model parameter extraction to simultaneously extract tens of parameters via derivative-free optimization. Traditionally, parameter extraction is performed manually by dividing the complete set of parameters into smaller subsets, each targeting different operational regions of the device, a process that can take several days or weeks. Our approach streamlines this process by employing derivative-free optimization to identify a good parameter set that best fits the compact model without performing an exhaustive number of simulations. We further enhance the optimization process to address three critical issues in device modeling by carefully choosing a loss function that focuses on relative errors rather than absolute errors to ensure consistent performance across different orders of magnitude, prioritizes accuracy in key operational regions above a specific threshold, and reduces sensitivity to outliers. Furthermore, we utilize the concept of train-test split to assess the model fit and avoid overfitting. We demonstrate the effectiveness of our approach by successfully modeling a diamond Schottky diode with the SPICE diode model and a GaN-on-SiC HEMT with the ASM-HEMT model. For the latter, which involves extracting 35 parameters for the ASM-HEMT DC model, we identified the best set of parameters in under 6,000 trials. Additional examples using both devices are provided to demonstrate robustness to outliers, showing that an excellent fit is achieved even with over 25% of the data purposely corrupted. These examples demonstrate the practicality of our approach, highlighting the benefits of derivative-free optimization in device modeling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
