ReviewiScience2020
Emerging Materials for Neuromorphic Devices and Systems.
Review in iScience, 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Controllable Charge Storability in InP/ZnSe Core/Shell Quantum Dots toward Bioinspired Optical Synaptic Application.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Edge-of-chaos operation and persistent dynamics for neuromorphic meminductor computing.Scientific reports · 2025Article
- Myelin-Proteinoids Interactions in Neural Signaling.Langmuir : the ACS journal of surfaces and colloids · 2025Article
- Article
- A Dual-Modal Memory Organic Electrochemical Transistor Implementation for Reservoir Computing.Small science · 2025Article
- Scalable Synaptic Transistor Memory from Solution-Processed Carbon Nanotubes for High-Speed Neuromorphic Data Processing.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Low-power artificial neuron networks with enhanced synaptic functionality using dual transistor and dual memristor.PloS one · 2025Article
- Editorial: Brain-inspired computing: from neuroscience to neuromorphic electronics for new forms of artificial intelligence.Frontiers in neuroscience · 2025Article
- Review
- Operating Mechanism Principles and Advancements for Halide Perovskite-Based Memristors and Neuromorphic Devices.The journal of physical chemistry letters · 2024Review
- Unveiling the Role of Side Chain for Improving Nonvolatile Characteristics of Conjugated Polymers-Based Artificial Synapse.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Spike Optimization to Improve Properties of Ferroelectric Tunnel Junction Synaptic Devices for Neuromorphic Computing System Applications.Nanomaterials (Basel, Switzerland) · 2023Article
- Multi-level resistive switching in hafnium-oxide-based devices for neuromorphic computing.Nano convergence · 2023Review
- Linear and symmetric synaptic weight update characteristics by controlling filament geometry in oxide/suboxide HfOScientific reports · 2023Article
- Emerging Memtransistors for Neuromorphic System Applications: A Review.Sensors (Basel, Switzerland) · 2023Review
- Spintronic leaky-integrate-fire spiking neurons with self-reset and winner-takes-all for neuromorphic computing.Nature communications · 2023Article
- Natural Organic Materials Based Memristors and Transistors for Artificial Synaptic Devices in Sustainable Neuromorphic Computing Systems.Micromachines · 2023Review
- Graphene and Beyond: Recent Advances in Two-Dimensional Materials Synthesis, Properties, and Devices.ACS nanoscience Au · 2022Review
- Bioactive Ion-Based Switchable Supercapacitors.Angewandte Chemie (International ed. in English) · 2022Article
- Synergy of Spin-Orbit Torque and Built-In Field in Magnetic Tunnel Junctions with Tilted Magnetic Anisotropy: Toward Tunable and Reliable Spintronic Neurons.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
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
No grant is acknowledged in the PubMed record.
Abstract
Neuromorphic devices and systems have attracted attention as next-generation computing due to their high efficiency in processing complex data. So far, they have been demonstrated using both machine-learning software and complementary metal-oxide-semiconductor-based hardware. However, these approaches have drawbacks in power consumption and learning speed. An energy-efficient neuromorphic computing system requires hardware that can mimic the functions of a brain. Therefore, various materials have been introduced for the development of neuromorphic devices. Here, recent advances in neuromorphic devices are reviewed. First, the functions of biological synapses and neurons are discussed. Also, deep neural networks and spiking neural networks are described. Then, the operation mechanism and the neuromorphic functions of emerging devices are reviewed. Finally, the challenges and prospects for developing neuromorphic devices that use emerging materials are discussed.
Indexed as
Identifiers
What OpenQuestion holds
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.