ReviewMaterials (Basel, Switzerland)2020
Memristive and CMOS Devices for Neuromorphic Computing.
Review in Materials (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed.
- Tunable Dynamics via Dual-Ion Modulation for Event-based Data Processing Using a Highly Uniform and Self-Rectifying Memristor Array.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Neuromorphic Near-Sensor and In-Sensor Computing Enabled by Next-Generation Material-Based Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A Review on Multi-Level Asymmetric Design for 2D Neuromorphic Devices.Nano-micro letters · 2026Review
- On the molecular basis of enduring memory in neurons, and cell fate in fibroblasts.Nucleic acids research · 2026Review
- On the Accuracy of Describing Polyelectrolyte Systems Based on Cross-Linked Networks in Terms of Linear Differential Equations.Polymers · 2026Article
- A Study on the Synaptic Behavior of Al/ZrOACS applied materials & interfaces · 2025Article
- Article
- Artificial sensory system based on memristive devices.Exploration (Beijing, China) · 2024Review
- Implementation of Artificial Synapse Using IGZO-Based Resistive Switching Device.Materials (Basel, Switzerland) · 2024Article
- Advances in Diagnostic Tools and Therapeutic Approaches for Gliomas: A Comprehensive Review.Sensors (Basel, Switzerland) · 2023Review
- Double-Forming Mechanism of TaOMaterials (Basel, Switzerland) · 2023Article
- Multi-neuron connection using multi-terminal floating-gate memristor for unsupervised learning.Nature communications · 2023Article
- Resistive random access memory: introduction to device mechanism, materials and application to neuromorphic computing.Discover nano · 2023Review
- SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks.Frontiers in neuroscience · 2023Article
- Review
- A high throughput generative vector autoregression model for stochastic synapses.Frontiers in neuroscience · 2022Article
- From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings.Frontiers in neuroinformatics · 2022Review
- Characterization and Programming Algorithm of Phase Change Memory Cells for Analog In-Memory Computing.Materials (Basel, Switzerland) · 2021Article
- Article
- In-Memory Logic Operations and Neuromorphic Computing in Non-Volatile Random Access Memory.Materials (Basel, Switzerland) · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, some major challenges have to be faced. Since the brain processes information by high-density neural networks with ultra-low power consumption, novel device concepts combining high scalability, low-power operation, and advanced computing functionality must be developed. This work provides an overview of the most promising device concepts in neuromorphic computing including complementary metal-oxide semiconductor (CMOS) and memristive technologies. First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed. Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. Finally, the main technology challenges and perspectives of neuromorphic computing will be discussed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.