13.1 A 1.33Tb 4-bit/Cell 3D-Flash Memory on a 96-Word-Line-Layer Technology

Shibata, Noboru; Kanda, K.; Shimizu, Tatsuo; Nakai, J.; Nagao, Osamu; Kobayashi, N.; Miakashi, M.; Nagadomi, Yasushi; Nakano, Takuya; Kawabe, Takahisa; Shibuya, Tadaharu; Sako, Mario; Yanagidaira, Kosuke; Hashimoto, Takahiro; Date, Hiroki; Sato, Manabu; Nakagawa, Takahiro; Takamoto, H.; Musha, Junji; Minamoto, Takatoshi; Uda, M.; Nakamura, Dai; Sakurai, Kazuo; Yamashita, Takahiro; Zhou, Jieyun; Tachibana, Ryoichi; Takagiwa, Teruo; Sugimoto, Tsuneaki; Ogawa, M.; Ochi, Yusuke; Kawaguchi, Kazuaki; Kojima, Masayasu; Ogawa, Takahisa; Hashiguchi, Tomoharu; Fukuda, R.; Masuda, Masami; Kawakami, Koichi; Someya, Tadashi; Kajitani, Yasuyuki; Matsumoto, Y.; Morozumi, N.; Sato, Jumpei; Raghunathan, Namas; Koh, Yee Lih; Chen, S.; Lee, J.; Nasu, Hiroaki; Sugawara, Hiroshi; Hosono, Koji; Hisada, T.; Kaneko, Tetsuya; Nakamura, Hiroki

doi:10.1109/isscc.2019.8662443

Cited by 33 publications

(10 citation statements)

References 3 publications

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“…Therefore, research efforts since 2015 have mainly turned to vertical stacking of the planar NAND flash arrays. This 3-D architecture, it is forecast, will drive the growth rate of the technology with the same pace through the next decade [1], [140].…”

Section: F Rtm As Solid State Drives (Ssds) Replacementmentioning

confidence: 99%

Magnetic Racetrack Memory: From Physics to the Cusp of Applications Within a Decade

et al. 2020

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| Racetrack memory (RTM) is a novel spintronic memory-storage technology that has the potential to overcome fundamental constraints of existing memory and storage devices. It is unique in that its core differentiating feature is the movement of data, which is composed of magnetic domain walls (DWs), by short current pulses. This enables more data to be stored per unit area compared to any other current technologies. On the one hand, RTM has the potential for mass data storage with unlimited endurance using considerably less energy than today's technologies. On the other hand, RTM promises an ultrafast nonvolatile memory competitive with static random access memory (SRAM) but with a much smaller footprint. During the last decade, the discovery of novel physical mechanisms to operate RTM has led to a major enhancement in the efficiency with which nanoscopic, chiral DWs can be manipulated. New materials and artificially atomically engineered thin-film structures have been found to increase the speed and lower the threshold current with which the data bits can be manipulated. With these recent Manuscript

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Section: F Rtm As Solid State Drives (Ssds) Replacementmentioning

confidence: 99%

Magnetic Racetrack Memory: From Physics to the Cusp of Applications Within a Decade

et al. 2020

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“…Then, critical limits led to the appearance of 3D memory [16] that entered the market in ~2015 and changed the conservative memory world. In 2020, vertical NAND stacked up to 96 layers, used QLC (quad level cells, four bits per cells) and reached a density of about 10 Gb/mm 2 , with typical memory capacities of one terabyte [17]. In 21 years, multilevel cell flash memory density has increased 10,000 times.…”

Section: Evolution Of Standalone Nonvolatile Memory Technologiesmentioning

confidence: 99%

Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence

Molas

Nowak

2021

Applied Sciences

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This paper presents an overview of emerging memory technologies. It begins with the presentation of stand-alone and embedded memory technology evolution, since the appearance of Flash memory in the 1980s. Then, the progress of emerging memory technologies (based on filamentary, phase change, magnetic, and ferroelectric mechanisms) is presented with a review of the major demonstrations in the literature. The potential of these technologies for storage applications addressing various markets and products is discussed. Finally, we discuss how the rise of artificial intelligence and bio-inspired circuits offers an opportunity for emerging memory technology and shifts the application from pure data storage to storage and computing tasks, and also enlarges the range of required specifications at the device level due to the exponential number of new systems and architectures.

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“…These challenges have provided the main motivation for our work -the development of VMM circuits and architectures based on 3D-NAND memories [34][35][36][37][38]. Indeed, even the already developed commercial 3D-NAND memory technology enables record-breaking effective bit density, ultra-low fabrication cost per bit, and multi-level cell programming capability [37], while still advancing rapidly.…”

Section: Introductionmentioning

confidence: 99%

3D-aCortex: an ultra-compact energy-efficient neurocomputing platform based on commercial 3D-NAND flash memories

Bavandpour

Sahay

Mahmoodi

et al. 2021

Neuromorph. Comput. Eng.

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We first propose an ultra-compact energy-efficient time-domain vector-by-matrix multiplier (VMM) based on commercial 3D-NAND flash memory structure. The proposed 3D-VMM uses a novel resistive successive integrate and re-scaling (RSIR) scheme to eliminate the stringent requirement of a bulky load capacitor which otherwise dominates the area- and energy-landscape of the conventional time-domain VMMs. Our rigorous analysis, performed at the 55 nm technology node, shows that RSIR-3D-VMM achieves a record-breaking area efficiency of ∼0.02 μm2/Byte and the energy efficiency of ∼6 f J/Op for a 500 × 500 4-bit VMM, representing 5× and 1.3× improvements over the previously reported 3D-VMM approach. Moreover, unlike the previous approach, the proposed VMM can be efficiently tailored to work in a smaller current output range. Our second major contribution is the development of 3D-aCortex, a multi-purpose neuromorphic inference processor that utilizes the proposed 3D-VMM block as its core processing unit. Rigorous performance modeling of the 3D-aCortex targeting several state-of-the-art neural network benchmarks has shown that it may provide a record-breaking 30.7 MB mm−2 storage efficiency, 113.3 TOp/J peak energy efficiency, and 10.66 TOp/s computational throughput. The system-level analysis indicates that the gain in the area-efficiency of RSIR leads to a smaller data transfer delay, which compensates for the reduction in the VMM throughput due to an increased input time window.

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13.1 A 1.33Tb 4-bit/Cell 3D-Flash Memory on a 96-Word-Line-Layer Technology

Cited by 33 publications

References 3 publications

Magnetic Racetrack Memory: From Physics to the Cusp of Applications Within a Decade

Magnetic Racetrack Memory: From Physics to the Cusp of Applications Within a Decade

Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence

3D-aCortex: an ultra-compact energy-efficient neurocomputing platform based on commercial 3D-NAND flash memories

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