MicroElec: High-throughput development and characterization of heterostructures for microelectronic applications

We propose to extend this so-called high-throughput combinatorial approach to multilayer microelectronic devices whose functional properties depend both on the nature of each of their constituent layers and on the interactions between these layers. Improving the performance of these devices amounts to globally optimizing the stacking of layers. Understanding and optimizing such complexity through artificial intelligence (AI)-assisted high-throughput synthesis, characterization and analysis processes of heterostructure libraries exhibiting systematic and simultaneous variation of key parameters would accelerate the development of efficient devices and massively supply materials databases. To validate this high-throughput AI-assisted approach in the context of microelectronics applications, existing combinatorial synthesis and characterization platforms will be strengthened and extended then used to define the most suitable lead-free compound/heterostructure for their integration into micrometric piezoelectric actuators as well as in nanometric ferroelectric non-volatile memories.