Results 61 to 70 of about 1,751 (142)

Neuronal Multi Unit Activity Processing with Metal Oxide Memristive Devices

Advanced Electronic Materials
Intra‐cortical brain‐machine interfaces (BMIs), able to decode neural activity in real‐time, represent a revolutionary opportunity for treating medical conditions.
Caterina Sbandati, Xiongfei Jiang, Deepika Yadav, Spyros Stathopoulos, Dana Cohen, Alex Serb, Shiwei Wang, Themis Prodromakis   +7 more
doaj   +1 more source

Probing the Critical Region of Conductive Filament in Nanoscale HfO₂ Resistive-Switching Device by Random Telegraph Signals [PDF]

Resistive-switching random access memory (RRAM) is widely considered as a disruptive technology. Despite tremendous efforts in theoretical modeling and physical analysis, details of how the conductive filament (CF) in metal-oxide-based filamentary RRAM ...
Chai, Z, Govoreanu, B, Ji, Z, Jurczak, M, Ma, J, Zhang, JF, Zhang, WD   +6 more
core   +1 more source

Ferroelectric HZO Thin Films for FEFETs: Crystal Structure‐Device Performance Relationship

Advanced Electronic Materials, Volume 12, Issue 1, 7 January 2026.
Crystal Structure of HZO Thin Films Methods of Thin Film Deposition 1T‐FeFET Design Influence on Ferroelectric Properties FeFET Applications Recent Advances and Challenges ORTHORHOMBIC HEARTBEAT. Abstract The rapid development of hafnium zirconium oxide (HZO) thin films has established ferroelectric field‐effect transistors (FeFETs) as strong ...
Harsha Ragini Aturi, Ramana Murthy Gajula, Deepshikha Rathore   +2 more
wiley   +1 more source

An RRAM-based implementation of a template matching circuit for low-power analogue classification

Frontiers in Electronics
Recent advances in machine learning and neuro-inspired systems enabled the increased interest in efficient pattern recognition at the edge. A wide variety of applications, such as near-sensor classification, require fast and low-power approaches for ...
Patrick Foster, Georgios Papandroulidakis, Alex Serb, Spyros Stathopoulos, Themis Prodromakis   +4 more
doaj   +1 more source

The Evolution of Gas Sensors Into Neuromorphic Systems

Advanced Electronic Materials, Volume 12, Issue 2, 21 January 2026.
Gas sensors are vital for various applications, but conventional designs rely on separate sensing, memory, and processing units, limiting speed, power efficiency, and adaptability. Neuromorphic gas sensing overcomes these constraints by integrating all functions in a single device.
Kevin Dominguez, Dhananjay D. Kumbhar, Abdul Momin Syed, Regina C. Martin, Nazek El‐Atab   +4 more
wiley   +1 more source

Neuromorphic Computing with Memcapacitors: Advancements, Challenges, and Future Directions

Advanced Electronic Materials, Volume 11, Issue 18, November 4, 2025.
Neuromorphic computing reduces energy costs by integrating memory and processing in event‐driven architectures, achieving energy usage as low as 10–30 pJ per operation for memcapacitor‐based synapses. Memcapacitors are reviewed as strong contenders for neuromorphic computing, enhancing AI acceleration through charge‐based computations, high resistance,
Nada AbuHamra, Muhammad Umair Khan, Eman Hassan, Mahmoud Al Qutayri, Baker Mohammad   +4 more
wiley   +1 more source

Towards a Memristor‐Based Circuit Implementation of the Hindmarsh–Rose Model

International Journal of Circuit Theory and Applications, Volume 53, Issue 11, Page 6712-6720, November 2025.
A variability‐aware simulation of the memristively approximated Hindmarsh–Rose neuron model demonstrates robust circuit behavior using realistic RRAM device characteristics, offering practical design guidance for neuromorphic hardware. ABSTRACT The transition from idealized memristor models to physical implementations, such as resistive random access ...
Sebastian Jenderny, Daniel Reiser, Karlheinz Ochs, Marc Reichenbach   +3 more
wiley   +1 more source

Interface Characterization of Plasma‐Treated InAs Electrodes for Resistive Random‐Access Memories Using Capacitance–Voltage Methods

physica status solidi (a), Volume 222, Issue 21, November 2025.
Resistive random‐access memories have previously been integrated on InAs nanowires for excellent memory scalability. It is challenging to do interface characterization on such devices due to the aggressive area scaling. In this article, the interface characteristics of larger test‐structures are studied to gain insight into what occurs in ultrascaled ...
André Andersen, Saketh Ram Mamidala, Lars‐Erik Wernersson   +2 more
wiley   +1 more source

What is next for LLMs? Pushing the boundaries of next‐gen AI computing hardware with photonic chips

Nanophotonics, Volume 14, Issue 22, Page 3499-3525, 01 November 2025.
Abstract Large language models (LLMs) are rapidly pushing the limits of contemporary computing hardware. For example, training GPT‐3 has been estimated to consume around 1,300 MWh of electricity, and projections suggest future models may require city‐scale (gigawatt) power budgets.
Renjie Li, Qi Xin, Wenjie Wei, Sixuan Mao, Erik Ma, Zijian Chen, Jingxing Gao, Malu Zhang, Haizhou Li, Zhaoyu Zhang   +9 more
wiley   +1 more source

A Compact CMOS Memristor Emulator Circuit and its Applications

, 2017
Conceptual memristors have recently gathered wider interest due to their diverse application in non-von Neumann computing, machine learning, neuromorphic computing, and chaotic circuits.
Saxena, Vishal
core   +1 more source