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Related Experiment Video

Updated: Apr 4, 2026

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

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Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System

Kapil Bhardwaj¹, Roshni Satheesh Babu², Yuxin Xia²

¹Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.

Advanced Materials (Deerfield Beach, Fla.)

|April 3, 2026

Summary

Neuromorphic electronics offer event-driven, parallel processing for scalable, energy-efficient Artificial Intelligence (AI) hardware. Advancements in materials and device integration are key for next-generation AI, especially for edge computing applications.

Keywords:

2D semiconductor ferroelectric memristors neuromorphic computing transistor

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Area of Science:

Materials Science
Computer Engineering
Artificial Intelligence

Background:

AI-driven automation demands more scalable, compact, and energy-efficient hardware.
Neuromorphic electronics, inspired by biological cognition, offer event-driven, parallel processing.
Emerging devices enable in-memory computation and integrated sensing beyond CMOS capabilities.

Purpose of the Study:

To highlight potential device technologies for next-generation neuromorphic AI hardware.
To showcase breakthroughs in robust, flexible, and conformable device platforms.
To discuss the necessity of advancing circuit and system design alongside device innovation.

Main Methods:

Perspective review of functional materials and unconventional computing architectures.
Showcasing key breakthroughs in device technologies for neuromorphic applications.
Discussion of circuit- and system-level design considerations for neuromorphic arrays.

Main Results:

Various device technologies show promise for next-generation neuromorphic AI hardware.
Robust, flexible, and conformable device platforms are crucial for edge applications.
Advancements in materials and device integration are critical research frontiers.

Conclusions:

Full-stack co-optimization from materials to algorithms is essential for adaptive, autonomous computing.
Neuromorphic electronics are particularly promising for resource-constrained edge platforms.
Integrated sensing and in-memory computation are key enablers for future AI hardware.