Research Topic

Ferroelectric Devices and Circuits for Neuromorphic Computing

About this Research Topic

The rapidly evolving field of Neuromorphic Computing has set off a paradigm of new applications for several emerging materials and devices. Ferroelectric materials and devices have been of interest in micro/nanoelectronics for decades. The hysteretic non-volatile polarization switching, and field-driven domain dynamics of ferroelectric materials can be harnessed to design artificial neurons and synapses. With the multitude of neuromorphic possibilities offered by ferroelectric material and devices, there remains a large set of design challenges. Different variants of ferroelectric devices (capacitor, transistor, tunnel junction) provide unique benefits and design opportunities. However, it is crucial to co-design the ferroelectric material, device, and overarching system/algorithm to realize a practical neuromorphic system. 

This research topic aims to present the latest innovations in ferroelectric technology that are specifically linked with the field of neuromorphic computing. The focus of this issue will be to explore the potential of leveraging emerging ferroelectric material and device functionalities to emulate various biological functionalities, along with the possibilities of envisioning memory-compute co-location to break the von-Neumann bottleneck. The issue welcomes articles in the domain of experimental demonstrations, device modeling, device-circuit-system-algorithm co-design, along with algorithm works that exploit ferroelectric properties. Different computing systems like deep learning, spiking networks, oscillatory systems, stochastic algorithms fall within the scope of the issue. Understanding the relative advantages/disadvantages of various ferroelectric device configurations in terms of neuromorphic design requirements (e.g., ON-OFF ratio, multi-level programmability, energy consumption, reliability, and endurance) will be critical to our understanding of ferroelectric neuromorphic computing.

We welcome manuscripts reporting novel research (experimental, numerical, and theoretical) on ferroelectric materials and devices that cater to the broad domain of neuromorphic computing. The topics of interest will comprise of (but will not be limited to) the following:

• Design of artificial neuromorphic primitives utilizing - Ferroelectric Capacitor (FE Cap), Ferroelectric Fied Effect Transistor (FeFET), Ferroelectric Tunnel Junction (FTJ)
• Novel computing systems leveraging ferroelectric device properties
• Device-circuit-architecture-algorithm co-design with ferroelectric technologies
• New insights on the design challenges in ferroelectric based neuromorphic systems
• Novel fabrication techniques for ferroelectric devices to facilitate neuromorphic system implementation
• Insights on variability and endurance limits of ferroelectric neuromorphic devices/circuits/systems


Keywords: Ferroelectric Capacitor, Ferroelectric Transistor, Ferroelectric Tunnel Junction, Polarization, Neuromorphic Computing, Artificial Neurons, Artificial Synapses, neural networks, brain-inspired computing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The rapidly evolving field of Neuromorphic Computing has set off a paradigm of new applications for several emerging materials and devices. Ferroelectric materials and devices have been of interest in micro/nanoelectronics for decades. The hysteretic non-volatile polarization switching, and field-driven domain dynamics of ferroelectric materials can be harnessed to design artificial neurons and synapses. With the multitude of neuromorphic possibilities offered by ferroelectric material and devices, there remains a large set of design challenges. Different variants of ferroelectric devices (capacitor, transistor, tunnel junction) provide unique benefits and design opportunities. However, it is crucial to co-design the ferroelectric material, device, and overarching system/algorithm to realize a practical neuromorphic system. 

This research topic aims to present the latest innovations in ferroelectric technology that are specifically linked with the field of neuromorphic computing. The focus of this issue will be to explore the potential of leveraging emerging ferroelectric material and device functionalities to emulate various biological functionalities, along with the possibilities of envisioning memory-compute co-location to break the von-Neumann bottleneck. The issue welcomes articles in the domain of experimental demonstrations, device modeling, device-circuit-system-algorithm co-design, along with algorithm works that exploit ferroelectric properties. Different computing systems like deep learning, spiking networks, oscillatory systems, stochastic algorithms fall within the scope of the issue. Understanding the relative advantages/disadvantages of various ferroelectric device configurations in terms of neuromorphic design requirements (e.g., ON-OFF ratio, multi-level programmability, energy consumption, reliability, and endurance) will be critical to our understanding of ferroelectric neuromorphic computing.

We welcome manuscripts reporting novel research (experimental, numerical, and theoretical) on ferroelectric materials and devices that cater to the broad domain of neuromorphic computing. The topics of interest will comprise of (but will not be limited to) the following:

• Design of artificial neuromorphic primitives utilizing - Ferroelectric Capacitor (FE Cap), Ferroelectric Fied Effect Transistor (FeFET), Ferroelectric Tunnel Junction (FTJ)
• Novel computing systems leveraging ferroelectric device properties
• Device-circuit-architecture-algorithm co-design with ferroelectric technologies
• New insights on the design challenges in ferroelectric based neuromorphic systems
• Novel fabrication techniques for ferroelectric devices to facilitate neuromorphic system implementation
• Insights on variability and endurance limits of ferroelectric neuromorphic devices/circuits/systems


Keywords: Ferroelectric Capacitor, Ferroelectric Transistor, Ferroelectric Tunnel Junction, Polarization, Neuromorphic Computing, Artificial Neurons, Artificial Synapses, neural networks, brain-inspired computing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

25 July 2021 Abstract
24 February 2022 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

25 July 2021 Abstract
24 February 2022 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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