In the last 40 years, the systematic downscaling of CMOS Integrated Circuit (IC) technologies has enabled unprecedented improvements in the transistor density, frequency of operation, energy efficiency and reliability. Most recent CMOS technologies allow the integration of several billions of transistors in a digital microprocessor chip the size of a fingernail. While technology downscaling has been extremely beneficial for digital circuits, the design of analog frontend electronics and Analog-to-Digital Converters (ADCs) in deep sub-micron CMOS technologies is becoming increasingly challenging due to the systematic power supply reduction, the intrinsically larger device parameter variability, and the higher low-frequency noise level of these transistors. To achieve high performance while ensuring a high level of reliability, complex and accurate calibration circuits need to be added to the ICs to counteract these effects. However, the design of these calibration circuits is challenging, and their effectiveness is typically limited to specific operating conditions.   

This project is done in cooperation with NXP semiconductors, Eindhoven.

Your duties

As a PhD researcher from the Integrated Circuits group, you will investigate a novel methodology to design and implement calibration techniques for Analog-to-Digital Converters with the aid of Machine Learning (ML). During your PhD you will first identify the root causes which limit ADC performance. Next you will investigate suitable ML algorithms to correct these errors, calibrate the ADC behavior and improve its performance. Finally, you will design a novel ADC with embedded ML calibration circuits, achieving beyond state-of-the-art performance.

In summary your main tasks will be:

• Analyze and identify the major root causes which limit the performance of specific state-of-the-art ADCs.
• Explore new design methodologies to post-correct errors in ADCs and enable smart calibrations using Machine Learning.
• Investigate methods to efficiently train the ML algorithms using a reduced set of data points.
• Demonstrate the potential and effectiveness of the proposed approach by designing, implementing and characterizing Analog-to-Digital Converters achieving a performance beyond current state-of-the-art.
• Dissemination of the results of your research in international and peer-reviewed journals and conferences.  
• Get involved in educational tasks such as the supervision of Master/Bachelor students and internships.
• Writing a dissertation based on the research outcomes and successfully defending it.