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Comprehensive Scaling Study on 3D Cross-Point PCM toward 1Znm Node for SCM Applications 面向1Znm节点的三维交叉点PCM综合缩放研究
Pub Date : 2019-06-09 DOI: 10.23919/VLSIT.2019.8776516
W. Chien, H. Ho, C. Yeh, C.H. Yang, H. Cheng, W. Kim, I. Kuo, L. Gignac, E. Lai, N. Gong, Y. Chou, C. Cheng, Y. Lin, J. Papalia, F. Carta, A. Rav, R. Bruce, M. Briahtxky, H. Lung
We present a scaling study toward lZnm node 3D Cross-point PCM (XPCM) for Storage Class Memory (SCM) applications. The low operation current, and low metal line loading resistance are desired to avoid a wide operation voltage distribution in a cross-point array. For the first time, AC threshold voltage (Vth) of 1S1R OTS-PCM was studied, which will impact the operation scheme. To achieve Tera bits per chip density, six layers 1Znm 3D XPCM with OTS showing high Vth and low leakage current, and scalable periphery circuit are required.
我们提出了一种用于存储类存储器(SCM)应用的lZnm节点三维交叉点PCM (XPCM)的缩放研究。低工作电流和低金属线负载电阻是为了避免在交叉点阵列中的宽工作电压分布。首次研究了1S1R OTS-PCM的交流阈值电压(Vth)对运行方案的影响。为了达到每芯片Tera位密度,需要6层具有高电压和低漏电流的具有OTS的1Znm 3D XPCM,以及可扩展的外围电路。
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引用次数: 11
Pre-shipment Data-retention/Read-disturb Lifetime Prediction & Aftermarket Cell Error Detection & Correction by Neural Network for 3D-TLC NAND Flash Memory 3D-TLC NAND闪存的出货前数据保留/读取干扰寿命预测和售后单元错误检测和校正神经网络
Pub Date : 2019-06-09 DOI: 10.23919/VLSIT.2019.8776480
Masaki Abe, Toshiki Nakamura, K. Takeuchi
This paper proposes 2 neural network (NN) techniques for 3D-TLC (Triple-Level Cell) NAND flash memory. 1) Predict data-retention/read-disturb lifetime for chip sorting during preshipment test. 2) Detect and correct errors in aftermarket. First, in pre-shipment test, Neural Network-based Lifetime Prediction (NNLP) predicts ECC decoding fail rate (EDFR) and estimates data-retention/read-disturb lifetime. Based on predicted lifetime, NNLP sorts NAND flash. Second, in aftermarket, Neural Network-based Error Detection (NNED) detects and corrects errors. NNED decreases bit-error rate (BER) by 81.4%.
提出了两种用于3D-TLC (Triple-Level Cell) NAND闪存的神经网络(NN)技术。1)预测出货前测试中芯片分拣的数据保留/读干扰寿命。2)发现并纠正售后的错误。首先,在出货前测试中,基于神经网络的寿命预测(NNLP)预测ECC解码失败率(EDFR)并估计数据保留/读取干扰寿命。基于预测寿命,NNLP对NAND闪存进行分类。其次,在售后市场,基于神经网络的错误检测(NNED)可以检测和纠正错误。NNED将误码率降低了81.4%。
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引用次数: 3
Fundamental Understanding and Control of Device-to-Device Variation in Deeply Scaled Ferroelectric FETs 深尺度铁电场效应管器件间变化的基本理解与控制
Pub Date : 2019-06-09 DOI: 10.23919/VLSIT.2019.8776497
K. Ni, W. Chakraborty, Jeffrey A. Smith, B. Grisafe, S. Datta
In this work, we present a comprehensive Kinetic Monte Carlo (KMC) modeling based statistical framework to evaluate the device-to-device variation of thin-film HfO2 ferroelectric FET (FeFET). We conclude that the closing of the memory window in a FeFET array with device scaling can be attributed to: 1) limited number of domains; 2) variation among domains; 3) intrinsic stochasticity of individual domain switching. To enable further scaling of FeFET, co-optimization approaches from material, process, and device operation to control variation are proposed: i) increase the number of domains through material/process optimization (e.g. decrease of deposition temperature, etc.); ii) improve the uniformity of domains (e.g. minimizing the domain size variation and defect distribution, etc.); iii) increase the pulse amplitude/width to ensure deterministic switching of individual domains.
在这项工作中,我们提出了一个全面的基于动力学蒙特卡罗(KMC)建模的统计框架来评估薄膜HfO2铁电场效应管(FeFET)器件间的变化。我们得出结论,在器件缩放的FeFET阵列中,内存窗口的关闭可归因于:1)有限的域数量;2)域间差异;3)个体域切换的固有随机性。为了实现FeFET的进一步缩放,提出了从材料、工艺和器件操作来控制变化的共同优化方法:i)通过材料/工艺优化(例如降低沉积温度等)增加畴的数量;Ii)改善域的均匀性(例如最小化域的尺寸变化和缺陷分布等);Iii)增加脉冲幅度/宽度,以确保各个域的确定性切换。
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引用次数: 44
Monolithic lntegratIon of GaAs//InGaAs photodetectors for multicolor detection 用于多色检测的GaAs/ InGaAs光电探测器的单片集成
Pub Date : 2019-06-09 DOI: 10.23919/VLSIT.2019.8776526
Dae-Myeong Geum, Sanghyeon Kim, S. Kim, S. Kang, J. Kyhm, J. Song, W. Choi, E. Yoon
Multicolor photodetectors (PDs) by using bulk p-i-n based visible GaAs and near-infrared (IR) InGaAs PD have been successfully fabricated via monolithic integration by wafer bonding and epitaxial lift-off. It showed high-performance individual operation comparable to that of bulk PDs with tight vertical alignment on a single substrate for future high-resolution multicolor PDs. At the same time, it covered a broad wavelength range from visible to IR.
利用大块p-i-n基可见GaAs和近红外InGaAs光电探测器,通过晶圆键合和外延升空的单片集成技术,成功制备了多色光电探测器。它显示了高性能的单独操作,可与在单一衬底上具有紧密垂直对齐的大块pd相媲美,可用于未来的高分辨率多色pd。同时,它覆盖了从可见光到红外的广泛波长范围。
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引用次数: 1
RRAM-based Spiking Nonvolatile Computing-In-Memory Processing Engine with Precision-Configurable In Situ Nonlinear Activation 基于随机存储器的峰值非易失性内存计算处理引擎,具有精确可配置的原位非线性激活
Pub Date : 2019-06-09 DOI: 10.23919/VLSIT.2019.8776485
Bonan Yan, Qing Yang, Wei-Hao Chen, Kung-Tang Chang, Jian-Wei Su, Chien-Hua Hsu, Sih-Han Li, Heng-Yuan Lee, S. Sheu, M. Ho, Qing Wu, Meng-Fan Chang, Yiran Chen, Hai Helen Li
This work presents a hybrid CMOS-RRAM integration of spiking nonvolatile computing-in-memory (nvCIM) processing engine (PE) that includes a 64Kb RRAM macro and a novel in situ nonlinear activation (ISNA) module. We integrate the computing controller and nonlinear activation function on-chip to compute convolutional or fully-connected neural network. ISNA merges A/D conversion and activation computation by leveraging its nonlinear working region. This eliminates the need for additional circuits to realize nonlinearity and reduces area by 43.7x w.r.t. the ADC scheme. The activation precision of ISNA can be configured from 1 to 8 bits to balance throughput, accuracy and power efficiency. The measurement of 4-layer LeNet shows such optimization improves 23.1% of computing speed via compromising a 2.5% relative accuracy drop. The proposed nvCIM PE achieves 16.9 TOPS/W power efficiency and a maximum spike frequency of 99.24 MHz.
这项工作提出了一种混合CMOS-RRAM集成的峰值非易失性内存计算(nvCIM)处理引擎(PE),其中包括一个64Kb的RRAM宏和一个新颖的原位非线性激活(ISNA)模块。我们在芯片上集成计算控制器和非线性激活函数来计算卷积或全连接神经网络。ISNA利用其非线性工作区域合并A/D转换和激活计算。这消除了额外电路来实现非线性的需要,并将ADC方案的面积减少了43.7倍的w.r.t.。ISNA的激活精度可配置为1 ~ 8位,以平衡吞吐量、精度和功率效率。对4层LeNet的测量表明,这种优化通过牺牲2.5%的相对精度下降,提高了23.1%的计算速度。所提出的nvCIM PE的功率效率为16.9 TOPS/W,最大尖峰频率为99.24 MHz。
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引用次数: 42
Superconducting parametric devices 超导参量器件
Pub Date : 2019-06-09 DOI: 10.23919/VLSIT.2019.8776576
T. Yamamoto
Superconducting parametric amplifier, originally developed more than half a century ago, gained renewed interests in some experiments of superconducting quantum electronics about a decade ago, and now has become an indispensable tool in the field of superconducting quantum information processing. More recently, there are several proposals, where a parametric oscillator, which is a parametric amplifier pumped above the threshold, is used as a quantum bit and the network of the parametric oscillators solve some computational tasks. Here, we briefly introduce the research activity on the development of the superconducting parametric devices, including our results, with some historical backgrounds.
超导参量放大器是半个多世纪前发展起来的,十多年前在超导量子电子学的一些实验中重新引起了人们的兴趣,现在已经成为超导量子信息处理领域不可或缺的工具。最近,有几种建议,其中一个参数振荡器,它是一个泵浦超过阈值的参数放大器,被用作量子比特,参数振荡器的网络解决一些计算任务。本文简要介绍了超导参量器件发展的研究活动,包括我们的研究成果,以及一些历史背景。
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引用次数: 0
Novel Quad interface MTJ technology and its first demonstration with high thermal stability and switching efficiency for STT-MRAM beyond 2Xnm 新颖的四接口MTJ技术及其在2Xnm以上的STT-MRAM中具有高热稳定性和开关效率的首次演示
Pub Date : 2019-06-09 DOI: 10.23919/VLSIT.2019.8776499
K. Nishioka, H. Honjo, S. Ikeda, T. Watanabe, S. Miura, H. Inoue, T. Tanigawa, Y. Noguchi, M. Yasuhira, H. Sato, T. Endoh
We have proposed novel quad-interface magnetic tunnel junction (MTJ) technology which brings forth an increase of both thermal stability factor $Delta$ and switching efficiency $Delta/I_{text{C}0}$ by a factor of 1.5-2 compared with conventional double-interface MTJ technology. We successfully fabricated the quad-interface MTJ using 300mm process based on novel low damage integration process including PVD, RIE and so on [1]. By developing the quad-interface MTJ, we have achieved about two times larger $Delta$ and $Delta/I_{text{C}0}$. Moreover, we have achieved about two times larger TMR ratio/RA by the stack development specific for the quad-interface MTJ technology. The developed quad-interface MTJ technology regarded as post-double-interface MTJ technology will become an essential technology for the scaling of the STT-MRAM beyond 20nm.
我们提出了一种新型的四界面磁隧道结(MTJ)技术,与传统的双界面MTJ技术相比,它的热稳定因子$Delta$和开关效率$Delta/I_{text{C}0}$都提高了1.5-2倍。我们基于新型的低损伤集成工艺,包括PVD、RIE等[1],采用300mm工艺成功制备了四界面MTJ。通过开发四接口MTJ,我们实现了大约两倍大的$Delta$和$Delta/I_{text{C}0}$。此外,通过针对四接口MTJ技术的堆栈开发,我们实现了大约两倍的TMR比/RA。所开发的四接口MTJ技术被称为后双接口MTJ技术,将成为STT-MRAM扩展到20nm以上的关键技术。
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引用次数: 19
Experimental Demonstration of Ferroelectric HfO2 FET with Ultrathin-body IGZO for High-Density and Low-Power Memory Application 用于高密度低功耗存储器的超薄体IGZO铁电HfO2场效应管的实验演示
Pub Date : 2019-06-09 DOI: 10.23919/VLSIT.2019.8776553
Fei Mo, Yusaku Tagawa, C. Jin, Min-Ju Ahn, T. Saraya, T. Hiramoto, M. Kobayashi
We have experimentally demonstrated a ferroelectric HfO2 FET with memory operation by introducing ultrathin IGZO as a channel material. Ultrathin-body IGZO ferroelectric FET (FeFET) shows high mobility with deposited channel material, nearly ideal subthreshold slope, and controllable memory characteristics with the use of back-end compatible process. These results are attributed to the properties of IGZO channel: junctionless FET operation, nearly-zero low-k interfacial layer on metal-oxide channel and good capping effect for realizing ferroelectric phase formation with HfZrO2. IGZO FeFET will open a new path for high-density memory application. Keywords: ferroelectric FET, HfO2, IGZO, memory.
我们通过引入超薄IGZO作为通道材料,实验证明了具有记忆操作的铁电HfO2场效应管。超薄体IGZO铁电场效应晶体管(FeFET)具有高迁移率、近理想的亚阈值斜率和使用后端兼容工艺的可控记忆特性。这些结果归功于IGZO沟道的特性:无结场效应管工作,金属氧化物沟道上接近零的低k界面层,以及与HfZrO2实现铁电相形成的良好封盖效应。IGZO效应场效应晶体管将为高密度存储器的应用开辟一条新的道路。关键词:铁电场效应晶体管,HfO2, IGZO,存储器。
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引用次数: 32
12-EUV Layer Surrounding Gate Transistor (SGT) for Vertical 6-T SRAM: 5-nm-class Technology for Ultra-Density Logic Devices 用于垂直6-T SRAM的12 euv层围绕栅极晶体管(SGT):用于超密度逻辑器件的5纳米级技术
Pub Date : 2019-06-09 DOI: 10.23919/VLSIT.2019.8776532
M. Kim, N. Harada, Y. Kikuchi, J. Boemmels, J. Mitard, T. Huynh-Bao, P. Matagne, Z. Tao, W. Li, K. Devriendt, L. Ragnarsson, C. Lorant, F. Sebaai, C. Porret, E. Rosseel, A. Dangol, D. Batuk, G. Martinez-Alanis, J. Geypen, N. Jourdan, A. Sepúlveda, H. Puliyalil, G. Jamieson, M. H. van der Veen, L. Teugels, Z. El-Mekki, E. Altamirano-Sanchez, Y. Li, H. Nakamura, D. Mocuta, F. Masuoka
For the first time, we establish a fabrication process flow of an EUV-era ultra-density 6-surrounding-gate-transistor SRAM with $0.0205 mu text{m}^{2}$ unit cell area and demonstrate nMOS surrounding-gate-transistor function. In this paper, 6-surrounding-gate-transistor SRAM design layout is shown, and the fabrication process flow and key process steps are explained in detail. NMOS functional device characteristics of surrounding-gate-transistor is analyzed.
我们首次建立了euv时代的超密度6-包围栅极SRAM的制造工艺流程,单元面积为0.0205 mu text{m}^{2}$,并演示了nMOS包围栅极晶体管的功能。本文给出了六围栅晶体管SRAM的设计版图,并对其制作工艺流程和关键工艺步骤进行了详细说明。分析了围栅晶体管的NMOS功能器件特性。
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引用次数: 7
A Ternary Based Bit Scalable, 8.80 TOPS/W CNN accelerator with Many-core Processing-in-memory Architecture with 896K synapses/mm2 一个基于二进制可扩展,8.80 TOPS/W的CNN加速器,具有多核内存处理架构,具有896K突触/mm2
Pub Date : 2019-06-09 DOI: 10.23919/VLSIT.2019.8776544
S. Okumura, M. Yabuuchi, K. Hijioka, Koichi Nose
A Processing-In-Memory (PIM) accelerator with ternary SRAM is proposed for low-power, large-scale deep neural network (DNN) processing. The accelerator consists of Ternary Neural Arithmetic Memory (TNAM) which is capable of bit-scalable MAC (multiply and accumulation) operation in accordance with target accuracy and power limit. An ADC less readout circuits to reduce analog-digital conversion power and a system-level variation avoidance technique utilizing features of TNAM are also proposed. A test chip with large-scale PIM is fabricated and successfully operate convolutional neural networks (CNNs) with 8.8TOPS/W and highest accuracy and area density among recent SRAM-type PIMs are obtained.
提出了一种基于三元SRAM的内存处理(PIM)加速器,用于低功耗、大规模深度神经网络(DNN)处理。该加速器由三元神经算术存储器(TNAM)组成,能够根据目标精度和功率限制进行位扩展的MAC(乘法和累加)操作。本文还提出了一种减少模数转换功率的无ADC读出电路和一种利用TNAM特性的系统级变差避免技术。制作了一个大规模PIM测试芯片,成功运行了卷积神经网络(cnn),其精度为8.8TOPS/W,是近年来sram型PIM中精度和面积密度最高的。
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引用次数: 34
期刊
2019 Symposium on VLSI Technology
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