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].