Heuristic Enz–Krummenacher–Vittoz (EKV) Model Fitting for Low-Power Integrated Circuit Design: An Open-Source Implementation

Accurate parameter extraction for the Enz–Krummenacher–Vittoz (EKV) model is crucial for low-power integrated circuit design, especially in weak and moderate inversion regions. This work introduces a novel iterative subranging (ISR) technique for EKV model fitting, implemented in Python (version 3.10.12) using SciPy (version 1.10.1), NumPy (version 1.24.3), and Matplotlib (version 3.7.1). The core of the methodology is the Fitter class, which refines the threshold voltage ((Formula presented.)) by progressively narrowing the fitting range, controlled by the fit_range_parameter. This approach achieves a relative fitting error below 5% within a continuous interval of drain current, ensuring accurate parameter extraction in the region of interest while considering the full data range. Validation using SkyWater130 NMOS data demonstrated that the ISR method covers an inversion coefficient (IC) range from (Formula presented.) to nearly 50, showcasing its ability to accurately model device behavior across weak, moderate, and strong inversion. Compared to state-of-the-art EKV extraction methods, the ISR method exhibited at least a ×2 reduction in fitting error within the weak inversion region. More importantly, the ISR method is easily tunable by the designer in order to focus on specific current regions, where a greater accuracy is desired. This is a distinctive characteristic of the ISR method not present in any other extraction procedure. Moreover, the method demonstrated strong robustness against measurement noise, maintaining accuracy even with a 1 nA RMS noise level. This work provides a powerful and accessible tool for EKV model parameter extraction, enhancing reproducibility and accuracy in analog circuit design for low-power applications.