This PhD offers a unique opportunity to enhance your skills in GaN power devices and develop cutting-edge architectures. You’ll work alongside a multidisciplinary team specializing in materials engineering, characterization, device simulation, and electrical measurements. If you’re eager to innovate, expand your knowledge, and tackle state-of-the-art challenges, this position is a valuable asset to your career!
Vertical GaN power components hold great promise for power applications beyond the kV range. Localized epitaxy of GaN enables the creation of thick structures on Si substrates at a competitive cost, with demonstrated success for diodes and pseudo-vertical transistors. However, this approach’s significant surface area limits the energy density of the devices. This PhD aims to develop denser, fully vertical components using layer transfer methods. You’ll study their electrical characteristics to monitor the impact of technological variations on their performance.
Throughout this PhD, you’ll gain comprehensive knowledge in microelectronics processes, electrical characterization, and TCAD (Technology Computer-Aided Design) simulation. You’ll collaborate with a multidisciplinary team including our partner CNRS-LTM and deepen your understanding of GaN power devices, all while being part of a lab dedicated to the development of wide-bandgap power devices. You will have the opportunity to write publications and patents.

With the rapid expansion of wireless networks and the imminent arrival of 6G, the need for highly efficient communication systems has never been more critical. In this context, frequencies beyond 140 GHz emerge as a key frontier, where cutting-edge technologies leverage advanced semiconductors like InP, delivering unmatched performance beyond what SiGe solutions can achieve. However, III-V components remain expensive, manufactured on small substrates (100 mm for InP), and incompatible with silicon production lines, which ensure higher industrial yields.
In this context, CEA-LETI, in collaboration with CNRS-LTM, is developing a new HBT transistor technology in which the base layer is made of antimonides, having already demonstrated frequency performance beyond the THz range. To enable integration with Si-CMOS fabrication processes, a novel approach for ohmic contact formation is required. This involves selective epitaxial regrowth of a suitable semiconductor material on the base layer of the HBT-GaAsSb transistor.
The PhD candidate will be responsible for identifying the optimal material that meets the required criteria, based on experiments conducted with the epitaxy team, advanced physical characterizations (ToF-SIMS, HR-TEM, EDX), and band structure modeling of the formed heterojunctions. This research will also be complemented by the fabrication of technological test structures to extract the key electrical parameters necessary for optimizing the DC and RF performance of the HBT transistor.

Silicon Carbide (SiC) is a semiconductor with superior intrinsic properties than Silicon for high temperature and high power electronics applications. SiC devices are expected to be extensively used in the electrification transition and novel energy management applications. To fully exploit the SiC superior properties, the future semiconductor devices will be used under extreme biasing and temperature conditions. These devices must operate safely at higher current densities, higher dV/dt and higher junction temperatures than Si devices does.
The objective of this thesis is to study the SiC devices fabricated at LETI under these extreme operating conditions, and to optimize their design to fully use the theoretical potential of SiC. The thesis work will include several phases that will be strongly coupled:
- Advanced electro-thermal characterisation (50%), by proposing new approaches to testing components in a box or on a suitable support, using artificial intelligence (AI) tools for data extraction and processing. The work will include adapting standard measurement methodologies to the specific switching characteristics of SiC.
- An assessment (15%) of the design and technological parameters responsible for the operating limits of the components.
- A physico-chemical characterisation component (15%) to analyse failures under these extreme conditions.
- The inclusion of predictive models (20%) for the sensitivity of architectures to extreme conditions and faults, based on modelling.

Neural Networks (NNs) are inspired by the brain’s computational and communication processes to efficiently address tasks such as data analytics, real time adaptive signal processing, and biological system modelling. However, hardware limitations are currently the primary obstacle to widespread adoption. To address this, a new type of circuit architecture called "neuromorphic circuit" is emerging. These circuits mimic neuron behaviour by incorporating high parallelism, adaptable connectivity, and in memory computation. Ion gated transistors have been extensively studied for their potential to function as artificial neurons and synapses. Even if these emerging devices exhibit excellent properties due to their ultra low power consumption and analog switching capabilities, they still need to be validated into larger systems.

At the RF and Energy Components Laboratory of CEA-Leti, we are developing new lithium-gated transistors as building blocks for deploying low-power artificial neural networks. After an initial optimization phase focused on materials and design, we are ready to accelerate the pace of development. These devices now need to be integrated into a real system to assess their actual performance and potential. In particular, both bio-inspired circuits and crossbar architectures for accelerated computation will be targeted.

During this 3-year PhD thesis, your (main) objective will be to design, implement, and test neural networks based on lithium-gated transistor crossbars (5x5, 10x10, 20x20) and neuromorphic circuits , along with the CMOS read and write logic to control them. The networks might be implemented using different algorithms and architectures, including Artificial Neural Network, Spiking Neural Networks and Recurrent Neural Networks, which will be then tested by solving spatial and/or temporal pattern recognition problems and reproduce biological functions such as pavlovian conditioning.