A Modern Framework to Incorporate Measured Load Pull Data in Power Amplifier Design

Load pull measurement and simulation are critical for high frequency power amplifier design. While extensive research has been done on load pull measurement and modeling techniques, little work has been given to standardizing the data. As a result, it often becomes overly complicated to use load pull data within simulation environments, or for model creation. A lack of uniformity leads to highly customized and varied post-processing. This disparity in data makes it difficult to reap the benefits of promising modeling techniques such as Artificial Neural Networks. These models are more difficult to create and validate if the training data has inconsistent structure or contains invalid observations. This paper proposes a software framework that includes unifying file reading algorithms and a universal in-memory data structure to store and manipulate load pull data.