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.