This work illustrates the analysis of Film Bulk Acoustic Resonators (FBAR) using 3D Finite Element (FEM) simulations with the software OnScale in order to predict and improve resonator performance and quality before manufacturing. This kind of analysis minimizes manufacturing cycles by reducing design time with 3D simulations running on High-Performance Computing (HPC) cloud services. It also enables the identification of manufacturing effects on device performance. The simulation results are compared and validated with a manufactured FBAR device, previously reported, to further highlight the usefulness and advantages of the 3D simulations-based design process. In the 3D simulation results, some analysis challenges, like boundary condition definitions, mesh tuning, loss source tracing, and device quality estimations, were studied. Hence, it is possible to highlight that modern FEM solvers, like OnScale enable unprecedented FBAR analysis and design optimization.

The typical Integrated Circuit (IC) development process commences with formulating specifications in natural language and subsequently proceeds to Register Transfer Level (RTL) implementation. RTL code is traditionally generated through manual efforts, using Hardware Description Languages (HDL) such as VHDL or Verilog. High-Level Synthesis (HLS), on the other hand, converts programming languages to HDL; these methods aim to streamline the engineering process, minimizing human effort and errors. Currently, Electronic Design Automation (EDA) algorithms have been improved with the use of AI, with new advancements in commercial (such as ChatGPT, Bard, among others) Large Language Models (LLM) and open-source tools presenting an opportunity to automate the chip design process. This paper centers on the creation of AI by AI, a Convolutional Neural Network (CNN) IC entirely developed by an LLM (ChatGPT-4), and its manufacturing with the first fabricable open-source Process Design Kit (PDK), SKY130A. The challenges, opportunities, advantages, disadvantages, conversation flow, and workflow involved in CNN IC development are presented in this work, culminating in the manufacturing process of AI by AI using a 130 nm technology, marking a groundbreaking achievement as possibly the world’s first CNN entirely written by AI for its IC manufacturing with a free PDK, being a benchmark for systems that can be generated today with LLMs.

Wireless Power Transfer (WPT) has emerged as a compelling alternative to wired charging; however, efficiency and safety are highly dependent on magnetic field distribution and leakage. This work presents a compact MEMS–based magnetic field induction sensor, including its design, fabrication, and electrical characterization, for real-time diagnosis in WPT systems. The sensor employs a Ni/Cr metallic inductor fabricated on a SiO₂ substrate using standard photolithography and occupies a footprint of 5 mm × 5 mm. The device is electrically characterized through impedance, quality factor, and frequency response measurements, followed by experimental validation using an industry-standard wireless charging system and dedicated signal-conditioning circuitry. The results from inductive coupling simulations, performed with the Magpylib Python library, align with experimental data showing that the sensor accurately follows theoretical magnetic field decay, detecting AC signals between 40 mV and 140 mV with a functional limit of 30 mm. Furthermore, experimental characterization through spatial mapping successfully identifies magnetic leakage hot spots, while thermal validation via infrared thermography correlates these magnetic readings with localized temperature increases. This integrated approach supports EMC optimization and thermal risk mitigation, providing a low–cost and effective diagnostic tool for enhancing safety and performance in WPT applications.