ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
Polarization Pruning: Reliability Enhancement of Hafnia-Based Ferroelectric Devices for Memory and Neuromorphic Computing.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Capacitive in-memory vector-matrix multiplication computing with charge-trap memcapacitors.Nano convergence · 2026Article
- Sustainable Synaptic Device with Two-Dimensional Ferroelectric Materials for Neuromorphic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Thermally activated excess noise by subgap density-of-states in Si-doped ZnSnO thin-film transistor-type gas sensor.Microsystems & nanoengineering · 2026Article
- Analysis of vertical AND flash memory for energy-efficient, scalable, fast CIM beyond vertical NAND flash memory.Nano convergence · 2026Article
- Noise Fingerprints as a Quantitative Order Parameter for Polarization- and Defect-Mediated Switching in Hafnia Ferroelectrics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Physical correlation between stochasticity and process-induced damage in ferroelectric memory devices.Nano convergence · 2025Article
- Ferroelectric NAND for efficient hardware bayesian neural networks.Nature communications · 2025Article
- Wordline Input Bias Scheme for Neural Network Implementation in 3D-NAND Flash.Biomimetics (Basel, Switzerland) · 2025Article
- Polarization Pruning: Reliability Enhancement of Hafnia-Based Ferroelectric Devices for Memory and Neuromorphic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Ferroelectric (FE) materials are key to advancing electronic devices owing to their non-volatile properties, rapid state-switching abilities, and low-energy consumption. FE-based devices are used in logic circuits, memory-storage devices, sensors, and in-memory computing. However, the primary challenge in advancing the practical applications of FE-based memory is its reliability. To address this problem, a novel polarization pruning (PP) method is proposed. The PP is designed to eliminate weakly polarized domains by applying an opposite-sign pulse immediately after a program or erase operation. Significant improvements in the reliability of ferroelectric devices are achieved by reducing the depolarization caused by weakly polarized domains and mitigating the fluctuations in the ferroelectric dipole. These enhancements include a 25% improvement in retention, a 50% reduction in read noise, a 45% decrease in threshold voltage variation, and a 72% improvement in linearity. The proposed PP method significantly improves the memory storage efficiency and performance of neuromorphic systems.
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.