Core Viewpoint - Keysight's ML Optimizer offers a revolutionary solution for semiconductor parameter extraction, addressing the complexities and inefficiencies of traditional optimization methods [2][29]. Group 1: Challenges in Parameter Extraction - The complexity of semiconductor device models has increased, making parameter extraction a significant challenge due to the large number of interrelated parameters [6][11]. - Traditional optimization algorithms, such as Newton-Raphson and Levenberg-Marquardt, often get trapped in local optima, leading to suboptimal extraction results [7][9]. Group 2: Introduction of ML Optimizer - Keysight introduced the ML Optimizer, which utilizes machine learning techniques to dynamically learn the optimization space, allowing for simultaneous optimization of over 40 parameters and multiple target plots [12][13]. - The ML Optimizer is designed to be robust against noise and does not rely on gradient information, making it more effective in non-convex spaces [12][13]. Group 3: Practical Applications and Benefits - In practical applications, the ML Optimizer demonstrated its efficiency by achieving good fitting for a diode model in approximately 300 trials, regardless of initial conditions [16]. - For the GaN HEMT model, the ML Optimizer completed parameter extraction in under 6000 trials within minutes, showcasing its speed and effectiveness [17]. - The optimizer enhances convergence and robustness through an integrated cost function, allowing it to handle complex models like BSIM4 and ASM-HEMT [18][19]. Group 4: Summary and Future Outlook - The ML Optimizer significantly simplifies the parameter extraction process, reducing modeling time from several days to just hours while improving fitting quality and consistency [29]. - The tool was showcased at IC-CAP 2025, with a recorded webinar available for further insights and demonstrations [23].

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].

如果您希望可以时常见面，欢迎标星收藏哦~ 在半导体技术飞速发展的今天，器件模型日益复杂，紧凑模型参数提取已成为业界面临的重 大挑战。传统优化算法受困于梯度变化不明确，极易陷入局部最优，但最终提取结果差强人 意的困局。此外，现代半导体模型中存在大量相互关联的参数，这使得传统方法效率更加低 下，建模工程师往往需要将参数提取流程拆解为多个冗长繁琐的子步骤，整个过程可能耗费 数天甚至数周时间，严重制约开发进度。 为 突 破 这 一 技 术 瓶 颈 ， 是 德 科 技 （ Keysight ） 重 磅 推 出 基 于 机 器 学 习 的 全 局 优 化 器 ——ML Optimizer，为半导体参数提取带来革命性解决方案！ 相较于传统方法，它能在单个步骤内同步处理海量图形与参数，极大简化参数提取流程，将原本漫 长的参数提取周期从数天大幅缩短至短短数小时，大幅提升工作效率。 此外，ML Optimizer 尤其擅长应对非凸参数空间，凭借先进的机器学习算法，它能突破传统方法 的局限，更精准地找到全局最优解，显著提升参数提取的准确性与整体拟合的一致性，为半导体器 件模型的精准构建提供坚实保障。 本次直播将介绍IC-CAP 和 ...

在半导体技术飞速发展的今天，器件模型日益复杂，紧凑模型参数提取已成为业界面临的重 大挑战。传统优化算法受困于梯度变化不明确，极易陷入局部最优，但最终提取结果差强人 意的困局。此外，现代半导体模型中存在大量相互关联的参数，这使得传统方法效率更加低 下，建模工程师往往需要将参数提取流程拆解为多个冗长繁琐的子步骤，整个过程可能耗费 数天甚至数周时间，严重制约开发进度。 为 突 破 这 一 技 术 瓶 颈 ， 是 德 科 技 （ Keysight ） 重 磅 推 出 基 于 机 器 学 习 的 全 局 优 化 器 ——ML Optimizer，为半导体参数提取带来革命性解决方案！ 是德科技 器件建模应用工程师 相较于传统方法，它能在单个步骤内同步处理海量图形与参数，极大简化参数提取流程，将原本漫 长的参数提取周期从数天大幅缩短至短短数小时，大幅提升工作效率。 此外，ML Optimizer 尤其擅长应对非凸参数空间，凭借先进的机器学习算法，它能突破传统方法 的局限，更精准地找到全局最优解，显著提升参数提取的准确性与整体拟合的一致性，为半导体器 件模型的精准构建提供坚实保障。 本次直播将介绍IC-CAP 和 MBP 提 ...