ArticleNature communications2023
Open-loop analog programmable electrochemical memory array.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Implementation of offset corrected AGAD algorithm on 130-nm CMOS technology-based RRAM array for analog neural network training.Science advances · 2026Article
- Wafer-scale SOT-MRAM for analog crossbar array applications.Science advances · 2026Article
- Advances in neuroprostheses: interfaces, materials, and applications.Nano convergence · 2026Review
- A reconfigurable photosensitive split-floating-gate memory for neuromorphic computing and nonlinear activation.Nature communications · 2026Article
- Update Disturbance-Resilient Analog ReRAM Crossbar Arrays for In-Memory Deep Learning Accelerators.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Near-infrared organic photoelectrochemical synaptic transistors by wafer-scale photolithography for neuromorphic visual system.Nature communications · 2025Article
- Hybrid functional 3D artificial synapses for convolution and reinforcement learning.Science advances · 2025Article
- Demonstration of transformer-based ALBERT model on a 14nm analog AI inference chip.Nature communications · 2025Article
- Resistive Switching Random-Access Memory (RRAM): Applications and Requirements for Memory and Computing.Chemical reviews · 2025Review
- Unveiling ECRAM switching mechanisms using variable temperature Hall measurements for accelerated AI computation.Nature communications · 2025Article
- Bio-plausible reconfigurable spiking neuron for neuromorphic computing.Science advances · 2025Article
- Darwin3: a large-scale neuromorphic chip with a novel ISA and on-chip learning.National science review · 2024Article
- Electrochemical random-access memory: recent advances in materials, devices, and systems towards neuromorphic computing.Nano convergence · 2024Review
- Analog Resistive Switching Devices for Training Deep Neural Networks with the Novel Tiki-Taka Algorithm.Nano letters · 2024Article
- A comprehensive review of advanced trends: from artificial synapses to neuromorphic systems with consideration of non-ideal effects.Frontiers in neuroscience · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Emerging memories have been developed as new physical infrastructures for hosting neural networks owing to their low-power analog computing characteristics. However, accurately and efficiently programming devices in an analog-valued array is still largely limited by the intrinsic physical non-idealities of the devices, thus hampering their applications in in-situ training of neural networks. Here, we demonstrate a passive electrochemical memory (ECRAM) array with many important characteristics necessary for accurate analog programming. Different image patterns can be open-loop and serially programmed into our ECRAM array, achieving high programming accuracies without any feedback adjustments. The excellent open-loop analog programmability has led us to in-situ train a bilayer neural network and reached software-like classification accuracy of 99.4% to detect poisonous mushrooms. The training capability is further studied in simulation for large-scale neural networks such as VGG-8. Our results present a new solution for implementing learning functions in an artificial intelligence hardware using emerging memories.
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.