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分析适用于细粒度存内逻辑器件的浮栅场效应晶体管的各种结构和温度特性。

Analyzing Various Structural and Temperature Characteristics of Floating Gate Field Effect Transistors Applicable to Fine-Grain Logic-in-Memory Devices.

作者信息

Cho Sangki, Kim Sueyeon, Kang Myounggon, Baik Seungjae, Jeon Jongwook

机构信息

Department of Electrical and Electronics Engineering, Konkuk University, Seoul 05029, Republic of Korea.

Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.

出版信息

Micromachines (Basel). 2024 Mar 27;15(4):450. doi: 10.3390/mi15040450.

DOI:10.3390/mi15040450
PMID:38675262
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11052355/
Abstract

Although the von Neumann architecture-based computing system has been used for a long time, its limitations in data processing, energy consumption, etc. have led to research on various devices and circuit systems suitable for logic-in-memory (LiM) computing applications. In this paper, we analyze the temperature-dependent device and circuit characteristics of the floating gate field effect transistor (FGFET) source drain barrier (SDB) and FGFET central shallow barrier (CSB) identified in previous papers, and their applicability to LiM applications is specifically confirmed. These FGFETs have the advantage of being much more compatible with existing silicon-based complementary metal oxide semiconductor (CMOS) processes compared to devices using new materials such as ferroelectrics for LiM computing. Utilizing the 32 nm technology node, the leading-edge node where the planar metal oxide semiconductor field effect transistor structure is applied, FGFET devices were analyzed in TCAD, and an environment for analyzing circuits in HSPICE was established. To seamlessly connect FGFET-based devices and circuit analyses, compact models of FGFET-SDB and -CSBs were developed and applied to the design of ternary content-addressable memory (TCAM) and full adder (FA) circuits for LiM. In addition, depression and potential for application of FGFET devices to neural networks were analyzed. The temperature-dependent characteristics of the TCAM and FA circuits with FGFETs were analyzed as an indicator of energy and delay time, and the appropriate number of CSBs should be applied.

摘要

尽管基于冯·诺依曼架构的计算系统已被使用很长时间,但其在数据处理、能耗等方面的局限性已引发了对适用于存内逻辑(LiM)计算应用的各种器件和电路系统的研究。在本文中,我们分析了先前论文中所识别出的浮栅场效应晶体管(FGFET)源漏势垒(SDB)和FGFET中心浅势垒(CSB)的温度相关器件及电路特性,并具体确认了它们在LiM应用中的适用性。与用于LiM计算的使用铁电体等新材料的器件相比,这些FGFET具有与现有的基于硅的互补金属氧化物半导体(CMOS)工艺更兼容得多的优势。利用32纳米技术节点,即应用平面金属氧化物半导体场效应晶体管结构的前沿节点,在TCAD中对FGFET器件进行了分析,并在HSPICE中建立了用于分析电路的环境。为了无缝连接基于FGFET的器件和电路分析,开发了FGFET - SDB和 - CSB的紧凑模型,并将其应用于用于LiM的三态内容可寻址存储器(TCAM)和全加器(FA)电路的设计。此外,还分析了FGFET器件在神经网络中的应用潜力。以FGFET的TCAM和FA电路的温度相关特性作为能量和延迟时间的指标进行了分析,并且应应用适当数量的CSB。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/09930a1fb087/micromachines-15-00450-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/8490dcf75743/micromachines-15-00450-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/394f337ea82c/micromachines-15-00450-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/ebb800ba6c84/micromachines-15-00450-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/704992f8fe66/micromachines-15-00450-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/6725e8a27426/micromachines-15-00450-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/3d48eefdf912/micromachines-15-00450-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/561947317b21/micromachines-15-00450-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/5c07cf0c037b/micromachines-15-00450-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/b265664967bb/micromachines-15-00450-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/4459be08f4d1/micromachines-15-00450-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/0492836a288c/micromachines-15-00450-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/09930a1fb087/micromachines-15-00450-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/8490dcf75743/micromachines-15-00450-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/394f337ea82c/micromachines-15-00450-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/ebb800ba6c84/micromachines-15-00450-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/704992f8fe66/micromachines-15-00450-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/6725e8a27426/micromachines-15-00450-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/3d48eefdf912/micromachines-15-00450-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/561947317b21/micromachines-15-00450-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/5c07cf0c037b/micromachines-15-00450-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/b265664967bb/micromachines-15-00450-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/4459be08f4d1/micromachines-15-00450-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/0492836a288c/micromachines-15-00450-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9765/11052355/09930a1fb087/micromachines-15-00450-g012.jpg

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