A 14-nm FinFET Logic CMOS Process Compatible RRAM Flash With Excellent Immunity to Sneak Path

Hsieh, E. R.; Kuo, Yen Chen; Cheng, Chien-Fu; Kuo, J. L.; Jiang, Meng-Ru; Lin, Jian-Li; Chen, Hung−Wen; Chung, Steve S.; Liu, Chuan–Hsi; Chen, Tse Pu; Huang, Shih An; Chen, Tai-Ju; Cheng, Osbert

doi:10.1109/ted.2017.2763960

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…The resistance is selected to be sufficiently larger than the LRS resistance ( ) and sufficiently smaller than the HRS resistance ( ); . This is possible because the ratio of to is several orders of magnitude in binary memristors (even if integrated with CMOS transistors 47 ). Voltage pulses are applied to the left terminal, inducing SETs in memristors serially from left to right.…”

Section: Resultsmentioning

confidence: 99%

Stochastic binary synapses having sigmoidal cumulative distribution functions for unsupervised learning with spike timing-dependent plasticity

Nishi

Nomura

Marukame

et al. 2021

Sci Rep

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Spike timing-dependent plasticity (STDP), which is widely studied as a fundamental synaptic update rule for neuromorphic hardware, requires precise control of continuous weights. From the viewpoint of hardware implementation, a simplified update rule is desirable. Although simplified STDP with stochastic binary synapses was proposed previously, we find that it leads to degradation of memory maintenance during learning, which is unfavourable for unsupervised online learning. In this work, we propose a stochastic binary synaptic model where the cumulative probability of the weight change evolves in a sigmoidal fashion with potentiation or depression trials, which can be implemented using a pair of switching devices consisting of serially connected multiple binary memristors. As a benchmark test we perform simulations of unsupervised learning of MNIST images with a two-layer network and show that simplified STDP in combination with this model can outperform conventional rules with continuous weights not only in memory maintenance but also in recognition accuracy. Our method achieves 97.3% in recognition accuracy, which is higher than that reported with standard STDP in the same framework. We also show that the high performance of our learning rule is robust against device-to-device variability of the memristor's probabilistic behaviour.

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Section: Resultsmentioning

confidence: 99%

Stochastic binary synapses having sigmoidal cumulative distribution functions for unsupervised learning with spike timing-dependent plasticity

Nishi

Nomura

Marukame

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…This process can be extended to FinFET platforms with 28 nm HKMG and beyond, effectively bridging the gap between high-performance logic and embedded memory. Additionally, this work showcased the great potential of embedded RRAM technology for achieving high temperature stability [153].…”

Section: Integration Of Embedded Rrammentioning

confidence: 79%

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

Wang,

Song,

Zhang

et al. 2024

Int. J. Extrem. Manuf.

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Embedded memory, which heavily relies on the manufacturing process, has been widely adopted in various industrial applications. As the field of embedded memory continues to evolve, innovative strategies are emerging to enhance performance. Among them, resistive random access memory (RRAM) has gained significant attention due to its numerous advantages over traditional memory devices, including high speed (<1ns), high density (4F2/n), high scalability (~nm), and low power consumption (~pJ). This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potential applications. It provides a brief introduction to the concepts and advantages of RRAM, discusses the key factors that impact its industrial manufacturing, and presents the commercial progress driven by cutting-edge nanotechnology, which has been pursued by many semiconductor giants. Additionally, it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things (IoT) and future intelligent computing, with a particular emphasis on its role in neuromorphic computing. Finally, the review discusses the current challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence (AI).

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“…Hsieh et al proposed a bipolar 14 nm node FinFET RRAM architecture and improved the ON/OFF window along with good endurance and retention performance. FinFET RRAM also reduces standby and active powers [129]. Magnetoresistive random-access memory (MRAM) is a non-volatile memory that stores data in magnetic domains.…”

Section: A Digital Circuit Interactionmentioning

confidence: 99%

FinFET to GAA MBCFET: A Review and Insights

Das,

Rajalekshmi,

James

2024

IEEE Access

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This review article presents a journey from Fin-shaped field effect transistor (FinFET) to gate-all-around multi-bridge channel field effect transistor (GAA MBCFET) technology, unraveling the evolution of semiconductor architectures. This article provides a concise yet insightful overview of the development of FinFET, exploring modified architectures, current trends, and associated constraints. The growing importance of other semiconductor materials instead of Si in FinFET or other technologies has been studied in detail. The article explores an emerging technology called 'GAA MBCFET', highlighting its advantages over FinFET. It also delves into the notable drawbacks and complex fabrication challenges associated with the upcoming GAA MBCFET technology. INDEX TERMS Gate-All-Around (GAA), Multi-Bridge Channel (MBC), Silicon on Insulator (SOI)

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A 14-nm FinFET Logic CMOS Process Compatible RRAM Flash With Excellent Immunity to Sneak Path

Cited by 12 publications

References 8 publications

Stochastic binary synapses having sigmoidal cumulative distribution functions for unsupervised learning with spike timing-dependent plasticity

Stochastic binary synapses having sigmoidal cumulative distribution functions for unsupervised learning with spike timing-dependent plasticity

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

FinFET to GAA MBCFET: A Review and Insights

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