A Study of the Effect of RRAM Reliability Soft Errors on the Performance of RRAM-Based Neuromorphic Systems

Tosson, Amr M. S.; Yu, Shimeng; Anis, Mohab; Wei, Lan

doi:10.1109/tvlsi.2017.2734819

Cited by 16 publications

(6 citation statements)

References 48 publications

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“…In addition, they emphasized that the retention effect is larger than the noise effect in terms of recognition rate error. Tosson et al provided a modeling framework to compute the effect of soft errors on the system accuracy [115]. They said that the soft error of RRAM is caused by the diffusion of the oxygen vacancies, unbalanced programming pulses and manufacturing defects.…”

Section: Rrammentioning

confidence: 99%

Emerging memory technologies for neuromorphic computing

et al. 2018

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In this paper, we reviewed the recent trends on neuromorphic computing using emerging memory technologies. Two representative learning algorithms used to implement a hardwarebased neural network are described as a bio-inspired learning algorithm and software-based learning algorithm, in particular back-propagation. The requirements of the synaptic device to apply each algorithm were analyzed. Then, we reviewed the research trends of synaptic devices to implement an artificial neural network.

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Section: Rrammentioning

confidence: 99%

Emerging memory technologies for neuromorphic computing

et al. 2018

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“…With Moore's law reaching its limits, scaling channel lengths beyond 10nm has become exceptionally challenging due to the direct tunneling between source and drain, which degrades gate control, switching functionality, and worsens power dissipation [2]. Fortunately, the use of new materials with exotic properties and new device's structure (TFET [3], FeFET [4], NcFET [5]) and memories (RRAM [6], MRAM [7], PCRAM [8]) have opened new avenues in circuit design [9]. We have seen open-source PDKs for CMOS and FinFET devices yet there is no precedent of an open-source PDK with emerging transistors.…”

Section: Introductionmentioning

confidence: 99%

FreePDK15TFET: An Open-Source Process Design Kit for 15nm CMOS and TFET devices

Chen

Zhang

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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With Moore's law reaching its limits, the use of new materials or new devices' structure has emerged as the next generation of CMOS devices. Among all, tunneling field-effect transistors (TFETs) have achieved a steep sub-threshold slope of less than 60mV/decade yet there is a lack of a complete process design kit (PDK) for large-scale circuit design. Hence, we present an open-source 15nm TFET PDK (15nmTFETPDK), which is based on FreePDK15 with additional support for open-source TFET models and its associated Cadence Virtuoso pcell in OA format and Mentor Calibre physical verification files. Our users can design their circuit in the Cadence Virtuoso platform and verify the consistency of the drawn layout and the circuit through the Calibre Platform. We hope that this open-source PDK allows them to further advance their research with new emerging devices.

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“…Several memory devices have been shown to have synaptic functions with a change in weight by recognizing signals from post-and preneurons. Two-terminal memristors, phase-change memory (PRAM), conductive bridge memory (CBRAM), and resistive change memory (RRAM) have been widely studied in the literature [10]- [28]. In particular, RRAM offers fast switching (~100 ns), low current operation (~1 µA), and a high-density structure with a crossbar array [29]- [34].…”

Section: Introductionmentioning

confidence: 99%

Zinc Tin Oxide Synaptic Device for Neuromorphic Engineering

et al. 2020

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Neuromorphic computing offers parallel data processing and low energy consumption and can be useful to replace conventional von Neumann computing. Memristors are two-terminal devices with varying conductance that can be used as synaptic arrays in hardware-based neuromorphic devices. In this research, we extensively investigate the analog symmetric multi-level switching characteristics of zinc tin oxide (ZTO)-based memristor devices for neuromorphic systems. A ZTO semiconductor layer is introduced between a complementary metal-oxide-semiconductor (CMOS) compatible Ni top electrode and a highly doped poly-Si bottom electrode. A variety of bio-realistic synaptic features are demonstrated, including long-term potentiation (LTP), long-term depression (LTD), and spike timing-dependent plasticity (STDP). The Ni/ZTO/Si device in which the adjustment of the number of states in conductance is realized by applying different pulse schemes is highly suitable for hardware-based neuromorphic applications. We evaluate the pattern recognition accuracy by implementing a system-level neural network simulation with ZTO-based memristor synapses. The density of states (DOS) and charge density plots reveal that oxygen vacancies in ZTO assist in generating resistive switching in the Ni/ZTO/Si device. The proposed ZTObased memristor composed of metal-insulator-semiconductor (MIS) structure is expected to contribute to future neuromorphic applications through further studies.

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A Study of the Effect of RRAM Reliability Soft Errors on the Performance of RRAM-Based Neuromorphic Systems

Cited by 16 publications

References 48 publications

Emerging memory technologies for neuromorphic computing

Emerging memory technologies for neuromorphic computing

FreePDK15TFET: An Open-Source Process Design Kit for 15nm CMOS and TFET devices

Zinc Tin Oxide Synaptic Device for Neuromorphic Engineering

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