与FinFET集成的四层3D垂直RRAM作为大脑启发认知信息处理的通用计算单元

Haitong Li, Kai-Shin Li, Chang-Hsien Lin, Juo-Luen Hsu, W. Chiu, Min-Cheng Chen, Tsung-Ta Wu, Joon Sohn, S. Eryilmaz, J. Shieh, W. Yeh, H.-S. Philip Wong
{"title":"与FinFET集成的四层3D垂直RRAM作为大脑启发认知信息处理的通用计算单元","authors":"Haitong Li, Kai-Shin Li, Chang-Hsien Lin, Juo-Luen Hsu, W. Chiu, Min-Cheng Chen, Tsung-Ta Wu, Joon Sohn, S. Eryilmaz, J. Shieh, W. Yeh, H.-S. Philip Wong","doi":"10.1109/VLSIT.2016.7573431","DOIUrl":null,"url":null,"abstract":"For the first time, a four-layer HfOx-based 3D vertical RRAM, the “tallest” one ever reported, is developed and integrated with FinFET selector. Uniform memory performance across four layers is obtained (±0.8V switching, 106 endurance, 104s@125°C). SPICE simulations show that high drive current of pillar select transistors is required for high-rise 3D RRAM arrays. The four-layer 3D RRAM is a versatile computing unit for (a) brain-inspired computing and (b) in-memory computing. (a) Stochastic RRAM synapses enable robust pattern learning for a 3D neuromorphic visual system. The 3D architecture with dense and balanced neuron-synapse connections provides 55% EDP savings and 74% VDD reduction (enhanced robustness) compared with conventional 2D architecture; (b) in-memory logic such as NAND, NOR, and bit shift, are essential elements for hyper-dimensional computing. Utilizing the unique vertical connection of 3D RRAM cells, these operations are performed with little data movement.","PeriodicalId":129300,"journal":{"name":"2016 IEEE Symposium on VLSI Technology","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"63","resultStr":"{\"title\":\"Four-layer 3D vertical RRAM integrated with FinFET as a versatile computing unit for brain-inspired cognitive information processing\",\"authors\":\"Haitong Li, Kai-Shin Li, Chang-Hsien Lin, Juo-Luen Hsu, W. Chiu, Min-Cheng Chen, Tsung-Ta Wu, Joon Sohn, S. Eryilmaz, J. Shieh, W. Yeh, H.-S. Philip Wong\",\"doi\":\"10.1109/VLSIT.2016.7573431\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For the first time, a four-layer HfOx-based 3D vertical RRAM, the “tallest” one ever reported, is developed and integrated with FinFET selector. Uniform memory performance across four layers is obtained (±0.8V switching, 106 endurance, 104s@125°C). SPICE simulations show that high drive current of pillar select transistors is required for high-rise 3D RRAM arrays. The four-layer 3D RRAM is a versatile computing unit for (a) brain-inspired computing and (b) in-memory computing. (a) Stochastic RRAM synapses enable robust pattern learning for a 3D neuromorphic visual system. The 3D architecture with dense and balanced neuron-synapse connections provides 55% EDP savings and 74% VDD reduction (enhanced robustness) compared with conventional 2D architecture; (b) in-memory logic such as NAND, NOR, and bit shift, are essential elements for hyper-dimensional computing. Utilizing the unique vertical connection of 3D RRAM cells, these operations are performed with little data movement.\",\"PeriodicalId\":129300,\"journal\":{\"name\":\"2016 IEEE Symposium on VLSI Technology\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"63\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE Symposium on VLSI Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VLSIT.2016.7573431\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE Symposium on VLSI Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VLSIT.2016.7573431","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 63

摘要

首次开发了一种基于hfox的四层3D垂直RRAM,这是迄今为止报道的“最高”的RRAM,并与FinFET选择器集成。在四层间获得均匀的存储性能(±0.8V开关,106续航时间,104s@125°C)。SPICE仿真结果表明,高层三维随机存储器阵列需要高驱动电流的选柱晶体管。四层3D RRAM是一种多功能计算单元,用于(a)大脑启发计算和(b)内存计算。(a)随机RRAM突触为三维神经形态视觉系统提供鲁棒模式学习。与传统的2D结构相比,具有密集和平衡的神经元-突触连接的3D结构可节省55%的EDP和减少74%的VDD(增强鲁棒性);(b)内存逻辑,如NAND、NOR和位移位,是超维计算的基本要素。利用3D RRAM单元独特的垂直连接,这些操作几乎不需要移动数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Four-layer 3D vertical RRAM integrated with FinFET as a versatile computing unit for brain-inspired cognitive information processing
For the first time, a four-layer HfOx-based 3D vertical RRAM, the “tallest” one ever reported, is developed and integrated with FinFET selector. Uniform memory performance across four layers is obtained (±0.8V switching, 106 endurance, 104s@125°C). SPICE simulations show that high drive current of pillar select transistors is required for high-rise 3D RRAM arrays. The four-layer 3D RRAM is a versatile computing unit for (a) brain-inspired computing and (b) in-memory computing. (a) Stochastic RRAM synapses enable robust pattern learning for a 3D neuromorphic visual system. The 3D architecture with dense and balanced neuron-synapse connections provides 55% EDP savings and 74% VDD reduction (enhanced robustness) compared with conventional 2D architecture; (b) in-memory logic such as NAND, NOR, and bit shift, are essential elements for hyper-dimensional computing. Utilizing the unique vertical connection of 3D RRAM cells, these operations are performed with little data movement.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信