A Neuromorphic Ferroelectric field-effect Ultra-Scaled Chip for Spiking Neural Networks

Are you passionate about cutting-edge technology and eager to push the boundaries of neuromorphic computing? We are seeking a highly motivated PhD student to join our research team, focused on developing ultra-scaled neuromorphic hardware utilizing ferroelectric/anti-ferroelectric field-effect transistors. Ferroelectric field-effect Transistors (FeFETs) are emerging devices, in which a ferroelectric capacitor is integrated in the gate stack of a baseline transistor above the dielectric. The negative capacitance behavior of the FeFET leads to unique characteristics: (i) sub 60mV/decade sub-threshold swing for low-power logic and (ii) non-volatile memory applications (thanks to the polarization retention in the absence of an electric field). The memory and computation integration ability in a single FeFET, supports persistent learning and long-term memory functions in neuromorphic circuits.

Our team aims to explore and leverage FeFET technologies to create advanced neuromorphic hardware that can mimic the brain's efficiency and scalability. This project involves modeling and hardware designing using FeFET devices, integrating them into neuromorphic circuits, and evaluating their performance in real-world applications. Qualifications: Educational Background: Master’s degree in Electronics Engineering or Computer Engineering (Hardware). Technical Expertise: Strong background in semiconductor device modeling, electronic devices, VLSI circuits, and hardware design. Experience with FeFET technology and non-silicon-based devices is a privilege. Skills: Proficiency in relevant software tools (HSPICE, Cadence, and physical design) and familiarity with programming languages (Verilog-A (is mandatory), Python, MATLAB). Strong analytical and problem-solving skills.