CHTM, University of New Mexico, 1313 Goddard St SE, Albuquerque, NM, 87106, USA.
ECE, University of New Mexico, 498 Terrace St NE, Albuquerque, NM, 87106, USA.
Sci Rep. 2023 Jun 27;13(1):10436. doi: 10.1038/s41598-023-36196-z.
Semiconductor device optimization using computer-based prototyping techniques like simulation or machine learning digital twins can be time and resource efficient compared to the conventional strategy of iterating over device design variations by fabricating the actual device. Ideally, simulation models require perfect calibration of material parameters for the model to represent a particular semiconductor device. This calibration process itself can require characterization information of the device and its precursors and extensive expert knowledge of non characterizable parameters and their tuning. We propose a hybrid method to calibrate multiple simulation models for a device using minimal characterization data and machine learning-based prediction models. A photovoltaic device is chosen as the example for this technique where optical and electrical simulation models of an industrially manufactured silicon solar cell are calibrated and the simulated device performance is compared with the measurement data from the physical device.
使用基于计算机的原型制作技术（如模拟或机器学习数字孪生）优化半导体器件，可以比通过制造实际器件迭代器件设计变体的传统策略更节省时间和资源。理想情况下，仿真模型需要对材料参数进行完美校准，以使模型能够代表特定的半导体器件。这个校准过程本身可能需要器件及其前体的特性化信息，以及对不可特性化参数及其调整的广泛专业知识。我们提出了一种使用最小特征化数据和基于机器学习的预测模型来校准多个仿真模型的混合方法。光伏器件被选为该技术的示例，其中对工业制造的硅太阳能电池的光学和电气仿真模型进行了校准，并将模拟器件性能与物理器件的测量数据进行了比较。
