SinoGreenTech Academic Portal
Official PDF TranslationSCIENCE CHINA Materials

Thriving In-Memory Computing and Neuromorphic Applications of Ferroelectric-Based Devices

Authors: WANG Xinglong; SONG Jiankang; NING Zhongfeng; ZHANG Wendi; JIANG Anquan

DOI: 10.1007/s40843-025-3874-1Status: Verified Translated Edition
Sponsored AdvertisementAd Placement Area
reCAPTCHA Bot Shield Active

Preparing Secure Academic Download

Verifying human reader & generating high-resolution document...

Verifying Document Integrity15s remaining
← Back to Article
Protected by Google reCAPTCHA v3.PrivacyTerms
Sponsored ContentAdSense In-Feed Ad Slot

Key Findings in This Report

• • Ferroelectric tunnel junctions (FTJs) based on Hf0.5Zr0.5O2 (HZO) achieve sub-nA operating currents (e.g., <1 nA) and fast switching speeds, enabling high-density, low-power artificial synapses for deep learning acceleration (Ref. 191). • • Ultrathin (3.5 nm) ferroelectric tunnel junctions demonstrate energy-efficient reservoir computing for temporal data learning, with robust performance metrics suitable for edge AI applications (Ref. 201). • • In-plane charged domain walls in ferroelectric films exhibit memristive behavior, offering a novel mechanism for analog synaptic weight modulation with potential for high-density crossbar arrays (Ref. 195). • • Ferroelectric domain wall memories and FTJs show enhanced tunneling electroresistance (TER) ratios, with values exceeding 1000% in optimized heterostructures, crucial for reliable multi-state storage in neuromorphic systems (Ref. 187).