The breakthrough of the scanning tunnelling microscope was soon followed by the advent of the atomic force microscope (AFM): this made it possible to study the topography of non-conducting samples. Meanwhile industry continued to reduce the size of transistors at the pace dictated by Moore's Law, reaching devices with truly nanometer-scale structures. Along this miniaturization trend, the atomic force microscope became indispensable in the semiconductor industry [1]. The determination of critical dimensions and surface roughness measurements became a standard task for an automated AFM system in industrial semiconductor testing applications.  

As another trend, semiconductor industry is advancing towards brain-inspired computing technologies, where the traditional von Neumann-type computing architectures are extended (or replaced) by novel neuromorphic computing schemes [2]. In this approach the information is usually stored and processed in analog tunable resistive memories, also known as memristors. AFM-based wafer testing is also becoming important in this area [3]. 

The PhD candidate will work on the development of novel AFM-based diagnostic methods addressing the challenges of the semiconductor industry. A key target of the PhD project is the AFM-based benchmarking of memristive systems. To this end a versatile conducting AFM methodology will be developed to record customized local current-voltage and noise characteristics on memristive thin film layers. This setup will be applied to investigate several memristive materials systems. The latter specific application will be broadened to more general areas of usage, where the basic AFM functionality will be extended with further local measurement methods (e.g. combining acquisition with electrical or microwave stimuli). In addition, the local probe measurements will be extended by environmental sensors, which will monitor unwanted disturbances, and thereby increase the yield of semiconductor characterization.  All these development activities will highly rely on advanced data-science aided automation approaches. 

The PhD project relies on the industry-university collaboration between Semilab Inc. and the Department of Physics of the Budapest University of Technology and Economics.  

[1] Lee, M.-K., Shin, M., Bao, T. et. al. Applications of AFM in semiconductor R&D and manufacturing at 45 nm technology node and beyond. Proc. SPIE 7272, Metrology, Inspection, and Process Control for Microlithography XXIII, 72722R (2009). https://doi.org/10.1117/12.813389

[2] Huang, Y., Ando, T., Sebastian, A. et al. Memristor-based hardware accelerators for artificial intelligence. Nat Rev Electr Eng 1, 286–299 (2024). https://doi.org/10.1038/s44287-024-00037-6

[3] Rao, M., Tang, H., Wu, J. et al. Thousands of conductance levels in memristors integrated on CMOS. Nature 615, 823–829 (2023). https://doi.org/10.1038/s41586-023-05759-5