ReviewNature nanotechnology2020
Neuromorphic nanoelectronic materials.
Review in Nature nanotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 150 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
150 citing papers in PubMed.
- Two-Dimensional Flash Memory: Materials, Interfaces, and Storage-Computing Convergence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Memristive singular value decomposition.Nature communications · 2026Article
- Organic semiconductor materials for neuromorphic and bioelectronic systems design rules and applications.iScience · 2026Review
- Organic Electrochemical Transistors for Neuromorphic Bioelectronics: Materials and Manufacturing Technologies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Defect-Engineered Floating-Gate Synapses With Programmable Relaxation Dynamics for Multi-Timescale Neuromorphic Computing.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Interfacial Engineering of MoSNanomaterials (Basel, Switzerland) · 2026Article
- WSAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Hysteresis Inversion Driven by Polar-Order Competition in van der Waals Ferrielectric.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Optoelectronic Nanofluidic Neural Networks for Ionic Computing.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Article
- Bio-Inspired Full-Spectrum Bidirectional Optoelectronic Synaptic Transistor for In-Sensor Neuromorphic Vision.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Topological acoustic synapse for high-dimensional neuromorphic computing.Science advances · 2026Article
- Stable Analog Weight Programming in Single-Crystalline van der Waals Ferroelectric Transistors for Reliable Computing-in-Memory.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Coherent quantum control of nitrogen vacancy spin with nanoscale magnets.Nature communications · 2026Article
- Polymer-Based Linear and Symmetric Artificial Synaptic Memristors for Accurate and Reliable Neuromorphic Computing Applications.Nanomaterials (Basel, Switzerland) · 2026Review
- Hybrid ferroelectric-ionic memristive hardware for high scalability in-memory computing.Nature communications · 2026Article
- Editor's Choice Articles in the Electronic Materials Section ofMaterials (Basel, Switzerland) · 2026Article
- Extreme Illuminated Vision Processing with a Graded Alloyed Perovskite In-sensor Computing Network.Nature communications · 2026Article
- Single-molecule neuromorphic device with aJ-level power consumption per switching.Nature communications · 2026Article
- HfOScience advances · 2026Article
90 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Memristive and nanoionic devices have recently emerged as leading candidates for neuromorphic computing architectures. While top-down fabrication based on conventional bulk materials has enabled many early neuromorphic devices and circuits, bottom-up approaches based on low-dimensional nanomaterials have shown novel device functionality that often better mimics a biological neuron. In addition, the chemical, structural and compositional tunability of low-dimensional nanomaterials coupled with the permutational flexibility enabled by van der Waals heterostructures offers significant opportunities for artificial neural networks. In this Review, we present a critical survey of emerging neuromorphic devices and architectures enabled by quantum dots, metal nanoparticles, polymers, nanotubes, nanowires, two-dimensional layered materials and van der Waals heterojunctions with a particular emphasis on bio-inspired device responses that are uniquely enabled by low-dimensional topology, quantum confinement and interfaces. We also provide a forward-looking perspective on the opportunities and challenges of neuromorphic nanoelectronic materials in comparison with more mature technologies based on traditional bulk electronic materials.
Indexed as
Identifiers
32123381What 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.