badcrossbar: A Python tool for computing and plotting currents and voltages in passive crossbar arrays

Joksas, Dovydas; Mehonić, Adnan

doi:10.1016/j.softx.2020.100617

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…However, the IR drop issues can be radically suppressed by implementing low conductance memristors such as FTJs in the crossbar array. To assess the scalability of the FTJ array in regard to IR drop we use the open‐source tool developed by Joksas et al [ 39 ] The wire resistance used in the simulation is calculated as

r = \rho \times 2 F A^{- 1} = 2 \rho F^{- 1}

where ρ is the metal resistivity, F the feature size which controls the cell‐cell pitch 2F and the wire cross section A = 2 F . We assume a tungsten wire with ρ = 10 μΩ cm [ 40 ] and a feature size F = 20 nm from which r = 10 Ω is obtained.…”

Section: Resultsmentioning

confidence: 99%

Ferroelectric Tunnel Junction Memristors for In‐Memory Computing Accelerators

Athle,

Borg

2023

Advanced Intelligent Systems

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Neuromorphic computing has seen great interest as leaps in artificial intelligence (AI) applications have exposed limitations due to heavy memory access, with the von Neumann computing architecture. The parallel in‐memory computing provided by neuromorphic computing has the potential to significantly improve latency and power consumption. Key to analog neuromorphic computing hardware are memristors, providing non‐volatile multistate conductance levels, high switching speed, and energy efficiency. Ferroelectric tunnel junction (FTJ) memristors are prime candidates for this purpose, but the impact of the particular characteristics for their performance upon integration into large crossbar arrays, the core compute element for both inference and training in deep neural networks, requires close investigation. In this work, a W/Hf x Zr1−x O2/TiN FTJ with 60 programmable conductance states, a dynamic range (DR) up to 10, current density >3 A m−2 at V read = 0.3 V and highly nonlinear current–voltage (I–V) characteristics (>1100) is experimentally demonstrated. Using a circuit macro‐model, the system level performance of a true crossbar array is evaluated and a 92% classification accuracy of the modified nation institute of science and technology (MNIST) dataset is achieved. Finally, the low on conductance in combination with the highly nonlinear I–V characteristics enable the realization of large selector‐free crossbar arrays for neuromorphic hardware accelerators.

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r = \rho \times 2 F A^{- 1} = 2 \rho F^{- 1}

Section: Resultsmentioning

confidence: 99%

Ferroelectric Tunnel Junction Memristors for In‐Memory Computing Accelerators

Athle,

Borg

2023

Advanced Intelligent Systems

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“…[125] Even more difficult to tackle are nonidealities that result in deviations from the linear (with respect to conductance and/or voltage) behavior, which DPEs rely on; such nonidealities include I-V nonlinearity [126,127] and line resistance. [128][129][130] There are multiple ways of utilizing DPEs for the implementation of ANNs. The most obvious one has been alluded to earlier-neural network weights may be mapped onto crossbar conductances after they have been trained on digital computers.…”

Section: Artificial Neural Network On Crossbar Arraysmentioning

confidence: 99%

“…[ 124 ] Even more difficult to tackle are nonidealities that result in deviations from the linear (with respect to conductance and/or voltage) behavior, which DPEs rely on; such nonidealities include I–V nonlinearity [ 125,126 ] and line resistance. [ 127–129 ]…”

Section: Future Computing Hardwarementioning

confidence: 99%

Memristive, Spintronic, and 2D‐Materials‐Based Devices to Improve and Complement Computing Hardware

Joksas

AlMutairi

Lee

et al. 2022

Advanced Intelligent Systems

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In a data-driven economy, virtually all industries benefit from advances in information technology-powerful computing systems are critically important for rapid technological progress. However, this progress might be at risk of slowing down if the discrepancy between the current computing power demands and what the existing technologies can offer is not addressed. Key limitations to improving energy efficiency are the excessive growth of data transfer costs associated with the von Neumann architecture and the fundamental limits of complementary metal-oxide-semiconductor (CMOS) technologies, such as transistors. Herein, three approaches that will likely play an essential role in future computing systems are discussed: memristive electronics, spintronics, and electronics based on 2D materials. The authors present how these technologies may transform conventional digital computers and contribute to the adoption of new paradigms, like neuromorphic computing.

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“…become conductive) [129], experience random telegraph noise (RTN) [130,131] or programming variability [132], or have their conductance state drift over time [133]. Even more difficult to tackle are nonidealities that result in deviations from the linear (with respect to conductance and/or voltage) behaviour that DPEs rely on; such nonidealities include I-V nonlinearity [134,135] and line resistance [136][137][138].…”

Section: Artificial Neural Network On Crossbar Arraysmentioning

confidence: 99%

Memristive, Spintronic, and 2D-Materials-Based Devices to Improve and Complement Computing Hardware

Joksas,

AlMutairi,

Lee

et al. 2022

Preprint

Self Cite

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In a data-driven economy, virtually all industries benefit from advances in information technology-powerful computing systems are critically important for rapid technological progress. However, this progress might be at risk of slowing down if we do not address the discrepancy between our current computing power demands and what the existing technologies can offer. Key limitations to improving the energy efficiency are the excessive growth of data transfer costs associated with the von Neumann architecture and the fundamental limits of complementary metal-oxide-semiconductor (CMOS) technologies, such as transistors. In this perspective article, we discuss three technologies that will likely play an essential role in future computing systems: memristive electronics, spintronics, and electronics based on 2D materials. We present some representative applications for each and speculate how these could fit within future digital, quantum and neuromorphic systems.

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badcrossbar: A Python tool for computing and plotting currents and voltages in passive crossbar arrays

Cited by 3 publications

References 11 publications

Ferroelectric Tunnel Junction Memristors for In‐Memory Computing Accelerators

Ferroelectric Tunnel Junction Memristors for In‐Memory Computing Accelerators

Memristive, Spintronic, and 2D‐Materials‐Based Devices to Improve and Complement Computing Hardware

Memristive, Spintronic, and 2D-Materials-Based Devices to Improve and Complement Computing Hardware

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