publications

2024

1. 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

   Abs

   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.

2. Heracles: A HfO2 Ferroelectric Capacitor Compact Model for Efficient Circuit Simulations

   Luca Fehlings, Md Hanif Ali, Paolo Gibertini, and 4 more authors

   arXiv preprint arXiv:2410.07791, May 2024

   Abs PDF

   This paper presents a physics-based compact model for circuit simulations in a SPICE environment for HfO2-based ferroelectric capacitors (FeCaps). The model has been calibrated based on experimental data obtained from HfO2-based FeCaps. A thermal model with an accurate description of the device parasitics is included to derive precise device characteristics based on first principles. The model incorporates statistical data that enables Monte Carlo analysis based on realistic distributions, thereby making it particularly well-suited for design-technology co-optimization (DTCO). Furthermore, the model is demonstrated in circuit simulations using an integrated circuit with current programming, wherein partial switching of the ferroelectric polarization is observed. Finally, the model was benchmarked in an array simulation, reaching convergence in 1.8 s with an array size of 100 kb.

2022

1. 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

   Abs PDF

   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.