Project Leadership and Work Package Leaders
Overall management of the "GaN-Unipress" project. Leader of WP3 – development of single-photon sources and quantum sensors based on GaN doped with rare-earth ions, design of device structures, and optical characterization.
Leader of WP1 – development of ammonothermal GaN single crystal growth towards 4-inch substrates with high structural quality and low resistivity, seed expansion, slicing, polishing, and substrate quality assurance.
Leader of WP2 – design and fabrication of advanced vertical power diodes (p-n, p-i-n, Schottky) on GaN substrates using epitaxy, ion implantation, and ultra-high pressure annealing (UHPA) dopant activation.
Leader of WP4 – applying artificial intelligence and machine learning (AI/ML) methods to optimize crystal growth processes, integrating experimental and simulation datasets, and creating the digital twin of ammonothermal crystallization.
Research and Technical Team
(Listed in alphabetical order by surname)
Preparation and mechanical processing of GaN crystals and substrates – cutting, grinding, edge profiling, mechanical and chemical-mechanical polishing (CMP) to epi-ready finish, chemical cleaning, and material documentation.
Device-level numerical simulations, investigation of hole transport mechanisms in GaN power diodes, and coupling computational insights with experimental fabrication runs.
Horizontal supervision over R&D commercialization, intellectual property (IP) protection, exploitation potential assessment, patent filing, market analyses, and negotiating partnerships with industrial stakeholders.
Structural characterization and packaging of GaN crystals, seeds, and wafers – optical microscopy inspection (VIS/UV, polarized light), interferometric surface profiling, defect mapping, and substrate qualification.
Electrical and optoelectronic characterization of diode structures and GaN-on-GaN semiconductor power devices.
Preparation of GaN samples for ion implantation, operation and execution of UHPA high-pressure annealing cycles (JBS diodes and GaN:REE), lithography and metallization processes, and WP4 database support.
Designing experimental and process data pipelines, developing and validating AI/ML models for predictive analysis and process optimization (including reduced-order surrogate models for the digital twin), and implementing computational workflows.
Execution of semiconductor fabrication runs and epitaxial growth (MOVPE) of gallium nitride heterostructures, along with technical support in device manufacturing.
Planning and setting up ammonothermal crystallization runs, seed selection and assembly in high-pressure autoclaves, parameter tuning (temperature, pressure, duration), post-growth analysis, and specimen evaluation.