Ferroelectric-based leaky integrate-and-fire neurons: A review on device design and performance optimization
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PUBLIC SUMMARY
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Ferroelectric devices are promising candidates for implementing leaky integrate-and-fire (LIF) neurons.
Foundational overview and device designs of ferroelectric/antiferroelectric neurons are introduced.
Key performance metrics and optimization strategies are discussed.
Applications of ferroelectric-based neurons for high-performance neuromorphic computing are presented.
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ABSTRACT
The leaky integrate-and-fire (LIF) neurons implemented in hardware have been proposed as a key approach for neuromorphic computing, offering a promising pathway to overcome the limitations of traditional von Neumann architectures. Among various candidates, ferroelectric-based neuromorphic devices (including antiferroelectric devices) offer a compact, energy-efficient, and highly scalable neuromorphic hardware, making them promising candidates for LIF neurons. This review systematically explains ferroelectric-based LIF neurons, covering the fundamental principles of neuronal operation, the implementation of neuronal functionalities, the key performance metrics, and strategies for performance optimization. Specifically, the implementation of neuronal functionalities is discussed focusing on the realization of leaky behavior by introducing depolarizing or inducing antiferroelectric phase to achieve volatility, since the neuronal integration and firing behaviors can be easily mimicked through the inherent cumulative polarization switching. Moreover, the key performance metrics, including hardware cost, energy consumption, and endurance of devices are identified to demonstrate the comprehensive advantages of ferroelectric LIF neurons. Additionally, the review also covers the applications of ferroelectric LIF neurons. Finally, this review summarizes challenges and prospects of ferroelectric-based artificial neurons for advanced neuromorphic computing systems. This review aims to provide theoretical guidance and practical insights to support further progress in neuromorphic computing systems based on ferroelectric materials. -
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REFERENCES
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Lu M., Wang J., Fang H., et al. (2026). Ferroelectric-based leaky integrate-and-fire neurons: A review on device design and performance optimization. The Innovation Materials 4:100202. https://doi.org/10.59717/j.xinn-mater.2026.100202
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Lu M., Wang J., Fang H., et al. (2026). Ferroelectric-based leaky integrate-and-fire neurons: A review on device design and performance optimization. The Innovation Materials 4:100202. https://doi.org/10.59717/j.xinn-mater.2026.100202
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Figure 1.
Comparison of biological neuron and the LIF neuron
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Figure 2.
The device design and LIF implementation of ferroelectric neurons
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Figure 3.
Field-induced programmable neuron response via antiferroelectric-ferroelectric phase transition and volatility
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Figure 4.
The device design and LIF implementation of antiferroelectric neurons
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Figure 5.
The hardware cost and endurance of ferroelectric neurons
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Figure 6.
The applications of ferroelectric LIF neurons
