ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022
High On/Off Ratio Spintronic Multi-Level Memory Unit for Deep Neural Network.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Resistive Switching Behavior of Sol-Gel-Processed ZnMgO/ZnO Bilayer in Optoelectronic Devices.Nanomaterials (Basel, Switzerland) · 2025Article
- Analytical and empirical correlation between magnetic domain wall creep behavior and fundamental magnetic properties.Scientific reports · 2025Article
- A Sliding-Kernel Computation-In-Memory Architecture for Convolutional Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Record high room temperature resistance switching in ferroelectric-gated Mott transistors unlocked by interfacial charge engineering.Nature communications · 2023Article
- High On/Off Ratio Spintronic Multi-Level Memory Unit for Deep Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
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Authors and funding
12 authors.
Funding
Abstract
Spintronic devices are considered as one of the most promising technologies for non-volatile memory and computing. However, two crucial drawbacks, that is, lack of intrinsic multi-level operation and low on/off ratio, greatly hinder their further application for advanced computing concepts, such as deep neural network (DNN) accelerator. In this paper, a spintronic multi-level memory unit with high on/off ratio is proposed by integrating several series-connected magnetic tunnel junctions (MTJs) with perpendicular magnetic anisotropy (PMA) and a Schottky diode in parallel. Due to the rectification effect on the PMA MTJ, an on/off ratio over 100, two orders of magnitude higher than intrinsic values, is obtained under proper proportion of alternating current and direct current. Multiple resistance states are stably achieved and can be reconfigured by spin transfer torque effect. A computing-in-memory architecture based DNN accelerator for image classification with the experimental parameters of this proposal to evidence its application potential is also evaluated. This work can satisfy the rigorous requirements of DNN for memory unit and promote the development of high-accuracy and robust artificial intelligence applications.
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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.