A Back-End-Of-Line Compatible, Ferroelectric Analog Non-Volatile Memory

A Ferroelectric, two-terminals, analog memristive device is fabricated with a Back-End-Of-Line, CMOS compatible process. A bilayer composed of a ferroelectric material, HfZrO4 (HZO) and a semiconducting oxide, WOx layer is comprised between two TiN electrodes. The devices demonstrate reversible and remanent resistive switching, with a record endurance (>10 10 switching cycles) and ON/OFF ratio up to 10. The analog resistive switching is obtained, with a cycle-to-cycle reproducibility of 90%. The synaptic behavior is explored with pulses of varying sign, length, or amplitude. For the scheme with constant pulse width the linearity coefficients for the potentiation and depression are -1.4 and 4. The dynamics of the resistive switching is shown to be governed by the ferroelectric domains switching. Finally, temperature dependent transport measurements indicate Ohmic conduction at low bias and Modified Schottky Emission at larger bias. They provide insights on the role of defects and oxygen vacancies in the transport and resistive switching mechanisms.

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“…Part of this work was presented at IEEE EDTM (2021), see reference [34], as well as IEEE IMW (2021), see reference [35].…”

Section: Conference Proceedingsmentioning

confidence: 99%

Analog Resistive Switching in BEOL, Ferroelectric Synaptic Weights

Bégon‐Lours¹,

Halter

Popoff

et al. 2021

IEEE J. Electron Devices Soc.

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“…We assume that the weight can change in a linear fashion without any noise. Nonlinear and asymmetric behaviour is often observed in practice but can be linearized by various pulse duration or amplitude variation schemes [22]. Lithium ion-based memristors can have a high degree of linearity but with limited dynamic range [23].…”

Section: Comparison With Analog Deterministic Synapsesmentioning

confidence: 99%

Advantages of binary stochastic synapses for hardware spiking neural networks with realistic memristors

Sulinskas

Borg

2022

Neuromorph. Comput. Eng.

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Hardware implementing Spiking Neural Networks has the potential to provide transformative gains in energy efficiency and through-put for energy-restricted machine-learning tasks. This is enabled by large arrays of memristive synapse devices that can be realized by various emerging memory technologies. But in practice, the performance of such hardware is limited by non-ideal features of the memristor devices such as non-linear and asymmetric state updates, limited bit-resolution, limited cycling endurance and device noise. Here we investigate how stochastic switching in binary synapses can provide advantages compared to realistic analog memristors when using unsupervised training of SNNs via spike timing dependent plasticity. We find that the performance of binary stochastic SNNs is similar or even better than analog deterministic SNNs when you consider memristors with realistic bit-resolution as well in situations with considerable cycle-to-cycle noise. Furthermore, binary stochastic SNNs require many fewer weight updates to train, leading to superior utilization of the limited endurance in realistic memristive devices.

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“…However, perovskite-based FTJs unfortunately suffer poor compatibility with CMOS technology. Therefore, as Böscke et al demonstrated ferroelectricity in thin-film HfO 2 , a material already integrated in CMOS, research on FTJs picked up steam and there are now many examples of HfO 2 -based FTJs in the literature. ,,− Nevertheless, the performance of HfO 2 -based FTJs is still inferior to that of the perovskite-based device; with lower read currents, endurance, limited retention and small TER still restricting the technology. − Moreover, the diminishing ferroelectric behavior for films below 5 nm , is posing a challenge in improving the low read current of these devices. This has been attributed to a strongly increasing energy barrier for crystallization in ultrathin HfO 2 /ZrO 2 films .…”

Section: Introductionmentioning

confidence: 99%

Top Electrode Engineering for Freedom in Design and Implementation of Ferroelectric Tunnel Junctions Based on Hf_1–xZr_xO₂

Athle

Persson

Troian

et al. 2022

ACS Appl. Electron. Mater.

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Ferroelectric tunnel junctions (FTJs) based on ultrathin HfO 2 have great potential as a fast and energy-efficient memory technology compatible with complementary metal oxide semiconductors. FTJs consist of a ferroelectric film sandwiched between two distinct electrodes, the properties of which are intricately linked to the electrical properties of the FTJs. Here we utilize a W crystallization electrode (CE) to achieve a high and reproducible remanent polarization, combined with a metal replacement process in which the W is carefully removed and replaced by another top electrode (TE). In this way we separate the ferroelectric film properties from the device design and can thereby evaluate the effect of the TE work function (WF) and conduction band electron density (n e ) on the tunneling electroresistance (TER) and device reliability. We compare FTJs designed with a TiN bottom electrode and W, Cr, or Ni TE and find that the use of high electron density metals such as Ni or Cr as TE allows for an improved TER, albeit at the cost of reliability due to a large built-in electric field. To bypass this effect, a bilayer Cr/Ni TE is implemented, which allows for a high TER and minimal built-in field, leading to excellent retention and endurance beyond 10 8 cycles. The results presented here thus highlight a process flow for reliable design and implementation of FTJs.

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A Back-End-Of-Line Compatible, Ferroelectric Analog Non-Volatile Memory

Cited by 6 publications

References 7 publications

Analog Resistive Switching in BEOL, Ferroelectric Synaptic Weights

Analog Resistive Switching in BEOL, Ferroelectric Synaptic Weights

Advantages of binary stochastic synapses for hardware spiking neural networks with realistic memristors

Top Electrode Engineering for Freedom in Design and Implementation of Ferroelectric Tunnel Junctions Based on Hf_1–xZr_xO₂

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A Back-End-Of-Line Compatible, Ferroelectric Analog Non-Volatile Memory

Cited by 6 publications

References 7 publications

Analog Resistive Switching in BEOL, Ferroelectric Synaptic Weights

Analog Resistive Switching in BEOL, Ferroelectric Synaptic Weights

Advantages of binary stochastic synapses for hardware spiking neural networks with realistic memristors

Top Electrode Engineering for Freedom in Design and Implementation of Ferroelectric Tunnel Junctions Based on Hf1–xZrxO2

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Product

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About

Top Electrode Engineering for Freedom in Design and Implementation of Ferroelectric Tunnel Junctions Based on Hf_1–xZr_xO₂