Compact Modeling of Ferroelectric HfO₂ for Advanced Memory and Computing Applications
Luca Fehlings a, Paolo Gibertini a, Fernando Quintana a, Bojian Zhang a, Aradhana Dube a, Erika Covi a
a ZIAM & CogniGron, University of Groningen
Proceedings of MATSUS Fall 2025 Conference (MATSUSFall25)
D3 Brain-Inspired Computation: Memristors, Oscillators, and Networks - #NeuroComp
València, Spain, 2025 October 20th - 24th
Organizers: Juan Bisquert, Beatriz Noheda and Martin F. Sarott
Invited Speaker, Luca Fehlings, presentation 377
Publication date: 21st July 2025

Logic and memory technologies face increasing complexity as continued scaling necessitates consideration of multiple physical processes to increase device, circuit and system reliability. This complexity drives the need for Design-Technology-Co-Optimization (DTCO) and System-Technology-Co-Optimization (STCO) approaches [1]. In these approaches, systems, circuits and devices are co-designed to improve performance and face critical development challenges. Emerging applications in machine learning acceleration and neuromorphic computing require sophisticated simulations that exploit extended regimes of device behavior, such as low-power operation at low voltages. Memory devices in particular lack adequate compact models, which are required for circuit simulation and design. Yet, the importance of memory devices and their compact models grows with Compute-In-Memory approaches, as SRAM and embedded DRAM scaling reach fundamental limits [2]. However, when used for computation, non-volatile memory (NVM) devices are facing serious reliability challenges due to endurance limitations that need to be modelled accurately [3].

In this talk we present Heracles [4], an efficient and dynamic compact model for ferroelectric HfO2 that enables analog and digital circuit simulations of both FeCap and FeFET devices with CMOS circuits. It further allows accurate modelling and simulation of variability and mismatch in both devices and circuits. This enables modelling of reliability phenomena and the exploration of their effects on the performance of memory circuits [5]. The physics-based model parameters are extracted using experimentally acquired characterization data and Technology Computer-Aided Design (TCAD) simulations. This allows direct correlation between reliability and scaling challenges with underlying material properties, such as parasitics or defects. In conclusion, our modeling framework provides essential tools for advancing ferroelectric memory technologies in next-generation computing architectures.

This work was supported by the European Research Council (ERC) through the European's Union Horizon Europe Research and Innovation Programme under Grant Agreement No 101042585. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. The University of Groningen would like to acknowledge the financial support of the CogniGron research center and the Ubbo Emmius Funds.

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