Luca Fehlings

Hello! I am Luca Fehlings, a PhD student in the Bio-Inspired Circuits and Systems group at the University of Groningen. Currently, I research ferroelectric HfO2-based devices and their integration into conventional memory macros as well as neuromorphic CMOS circuits. Together with my colleagues I am realizing circuit building blocks that allow efficient memory storage as well as learning on biological time-scales in hardware.

My research interests cover the topics of process integration, compact modeling and electrical characterization of semiconductor and memory devices, particularly regarding their integrability and reliability.

I hold a B.Sc and M.Sc degree from RWTH Aachen in Electrical Engineering, which I received in 2019 and 2022. During that time I worked on photonics, 2D-materials and organic semiconductors.

selected publications

A Ferroelectric Tunnel Junction-based Integrate-and-Fire Neuron

P. Gibertini, L. Fehlings, S. Lancaster, and 6 more authors

In 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Oct 2022

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Event-based neuromorphic systems provide a low-power solution by using artificial neurons and synapses to process data asynchronously in the form of spikes. Ferroelectric Tunnel Junctions (FTJs) are ultra low-power memory devices and are well-suited to be integrated in these systems. Here, we present a hybrid FTJ-CMOS Integrate-and-Fire neuron which constitutes a fundamental building block for new-generation neuromorphic networks for edge computing. We demonstrate electrically tunable neural dynamics achievable by tuning the switching of the FTJ device.
Coincidence Detection with an Analog Spiking Neuron Exploiting Ferroelectric Polarization

Paolo Gibertini, Luca Fehlings, Thomas Mikolajick, and 3 more authors

In 2024 IEEE International Symposium on Circuits and Systems (ISCAS), May 2024

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The ability to detect correlated events in the environment is an important feat of biological neural networks. Neuromorphic computing strives to mimic this ability for efficient sensory processing. For this purpose, we propose a HfO2-based ferroelectric capacitor (FeCap)-complementary metal oxide semiconductor (CMOS) leaky integrate-and-fire (LIF) neuron able to detect highly correlated events exploiting two different temporal dynamics. The possibility to exploit two time constants increases the versatility of the neuron and its dynamic adaptation while offering a compact and elegant solution for detection of both transient and sustained coincidences. Moreover, the time constants are in biologically relevant time scales, which makes the neuron suitable to solve real-time tasks such as keyword spotting or sensory processing. The proposed FeCap-based LIF (FeLIF) neuron enriches the dynamic of a standard LIF neuron fostering the development of advanced event-based analog neuromorphic hardware.