RMS

The test, control and calibration of AMS-RF-mmW functions in a complex integrated system represent nowadays a major challenge for the IC industry. Our research in this area is focused on two main research lines: a) the development of AMS-RF-mmW state-of-the-art on-chip test instruments for Built-In Self-Test (BIST) applications and dedicated DfT techniques; and b) the development of embedded solutions for performance control, optimization and self-calibration.

Novel AMS/RF/mmW design solutions are required in a wide variety of state-of-the-art applications, including communications, computing, imaging, etc. In this regard, the RMS group explores the multiple challenges of state-of-the-art AMS/RF/mmW current and emerging design paradigms. Our research includes the development of low-power mixed-signal and RF design techniques, state-of-the-art data converters for imaging applications, integrated control electronics for quantum computing, and advanced RF and mmW design techniques for beyond- 5G and 6G applications.

The basis for using machine learning for AMS/RF circuits is to find rich statistical performance models which allow predicting the circuit performance from simple observational data. In this research line, the RMS group explores the use of machine learning techniques for reducing test complexity and cost, simplifying the control of complex systems and enabling efficient statistical calibration methods.