Fast and Accurate Automatic Design of Spiral Inductors from High-Level Specification - Ismail, Heba (AUC Knowledge Fountain, 2024)
Integrated on-chip planar spiral inductors are widely employed in communication circuit blocks and other various electronic devices, thus becoming an essential passive component of RF integrated circuits (RFICs). A common vital spiral inductor design problem is determining the optimum layout parameters of spiral inductors that yield the desired inductance value and achieve highest quality factor at the intended operating frequency. Various approaches tackle this difficulty, but the most prevalent approaches for designing inductors are using an electromagnetic simulator or a library of pre-designed inductors. The latter approach requires an extensive amount of time and computational resources, whereas the former restricts the design space and performance. A further approach that has been extensively emerging recently is undergoing optimization algorithms, such as sequential quadratic programming, simulated annealing, artificial neural network, genetic algorithms… etc. This thesis presents a simple, fast, and accurate automated design approach of spiral inductors from high-level specifications based on parametric modeling. Since this approach is based on modeling, two novel wide-band models are proposed for the overlap coupling capacitance of the adjunct coplanar strips. These two models are tested against the simulation results obtained from the dominant mainstream electromagnetic simulators HFSS and EMX and have showed enhanced accuracy over previously proposed models in literature especially at high frequencies. One of the proposed models was selected and implemented using a JavaScript software tool on Google Apps Script platform. The App has a user-friendly interface that automatically finds the optimal design of spiral inductors. This tool achieves the target user high-level specifications within a maximum error bound of seven percent when compared to the simulation results from EMX, which can reduce designing time and hence product time to market.