半导体参数提取，革命性解决方案

Core Viewpoint - The article discusses the challenges in semiconductor parameter extraction due to the complexity of device models and the limitations of traditional optimization algorithms, introducing Keysight's ML Optimizer as a revolutionary solution to enhance efficiency and accuracy in this process [1][4]. Group 1: Challenges in Semiconductor Parameter Extraction - The complexity of semiconductor device models has made parameter extraction increasingly challenging, with traditional optimization algorithms often getting stuck in local optima due to unclear gradient changes [1]. - The presence of numerous interrelated parameters in modern semiconductor models leads to inefficiencies in traditional methods, requiring engineers to break down the extraction process into lengthy sub-steps, which can take days or even weeks [1]. Group 2: Introduction of ML Optimizer - Keysight has launched the ML Optimizer, a machine learning-based global optimizer that simplifies the parameter extraction process by synchronously handling vast amounts of data in a single step, reducing the extraction time from days to just hours [2][4]. - The ML Optimizer excels in navigating non-convex parameter spaces, utilizing advanced machine learning algorithms to find global optima more accurately, thereby improving the precision of parameter extraction and the consistency of overall fitting [4]. Group 3: Live Demonstration and Applications - A live demonstration is scheduled to showcase the ML Optimizer's effectiveness in various device modeling tasks, including diodes, GaN HEMTs, MOSFETs, and BJTs, with interactive elements and prizes for participants [4][6]. - The event will feature experts from Keysight, including application engineers and product managers, who will discuss the application of artificial neural networks and the ML Optimizer in device modeling [8][11].