Efficient Probabilistic Computing with Stochastic Perovskite Nickelates

Park, Tae Joon; Selcuk, Kemal; Zhang, Haitian; Manna, Sukriti; Batra, Rohit; Wang, Qi; Yu, Haoming; Aadit, Navid Anjum; Sankaranarayanan, Subramanian K. R. S.; Zhou, Hua; Ramanathan, Shriram

doi:10.1021/acs.nanolett.2c03223

Cited by 14 publications

(14 citation statements)

References 28 publications

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“…The maximum frequency observed in pristine NNO is 596 cm –1 (shown in SI Figure S6), while an oxygen dominated mode appears at 601 cm –1 in the H 0.25 NdNiO 3 system. While the frequencies associated with hydrogen motion are likely to strongly vary in this system with many metastable states, our calculations clearly indicate that vibrational modes with frequencies in excess of 700 cm –1 are associated with OH stretching modes. − …”

Section: Resultsmentioning

confidence: 67%

Infrared Nanoimaging of Hydrogenated Perovskite Nickelate Memristive Devices

Gamage,

Manna,

Zajac

et al. 2024

ACS Nano

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Solid-state devices made from correlated oxides, such as perovskite nickelates, are promising for neuromorphic computing by mimicking biological synaptic function. However, comprehending dopant action at the nanoscale poses a formidable challenge to understanding the elementary mechanisms involved. Here, we perform operando infrared nanoimaging of hydrogen-doped correlated perovskite, neodymium nickel oxide (H-NdNiO 3 , H-NNO), devices and reveal how an applied field perturbs dopant distribution at the nanoscale. This perturbation leads to stripe phases of varying conductivity perpendicular to the applied field, which define the macroscale electrical characteristics of the devices. Hyperspectral nano-FTIR imaging in conjunction with density functional theory calculations unveils a real-space map of multiple vibrational states of H-NNO associated with OH stretching modes and their dependence on the dopant concentration. Moreover, the localization of excess charges induces an out-of-plane lattice expansion in NNO which was confirmed by in situ X-ray diffraction and creates a strain that acts as a barrier against further diffusion. Our results and the techniques presented here hold great potential for the rapidly growing field of memristors and neuromorphic devices wherein nanoscale ion motion is fundamentally responsible for function.

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Section: Resultsmentioning

confidence: 67%

Infrared Nanoimaging of Hydrogenated Perovskite Nickelate Memristive Devices

Gamage,

Manna,

Zajac

et al. 2024

ACS Nano

Self Cite

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“…Metastability of states is both useful and presents a challenge depending on the application domain. For example, it has been reported that protons may take on multiple metastable configurations in a lattice, resulting in probabilistic transport gaps . The inherent probabilistic property of a device can be utilized to build p-bits that serve as building blocks for probabilistic computing.…”

Section: Computational Exploration Of Metastable Phasesmentioning

confidence: 99%

Proton Conducting Neuromorphic Materials and Devices

Yuan,

Patel,

Banik

et al. 2024

Chem. Rev.

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Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotronbased spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.

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“…[78] (SOT). While many other implementations of p-bits are possible, from molecular nanomagnets [79] to diffusive memristors [80], RRAM [81], perovskite nickelates [82] and others, two additional advantages of the MRAM-based p-bits are the proven manufacturability (up to billion bit densities) and the amplification of room temperature noise. Even with the thermal energy of kT in the environment, magnetic switching causes large resistance fluctuations in MTJs, creating hundreds of millivolts of change in resistive dividers [70].…”

Section: Mixed-signalmentioning

confidence: 99%

“…Further improvements to hardware implementation include adaptive versions of PT [130] as well as sophisticated nonequilibrium Monte Carlo (NMC) algorithms [131]. Ideas involving overclocking p-bits such that they violate the t synapse ≪ ⟨T p-bit ⟩ requirement for further improvement [14] or sharing synaptic operations between p-bits [82] could also be useful. A combination of these ideas with algorithm-architecture-device co-design may lead to orders of magnitude improvement in sampling speeds and quality.…”

Section: Outlook: Algorithms and Applications Beyondmentioning

confidence: 99%

A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

Chowdhury

Grimaldi

Aadit

et al. 2023

IEEE J. Explor. Solid-State Comput. Devices Circuits

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The transistor celebrated its 75 th birthday in 2022. The continued scaling of the transistor defined by Moore's Law continues, albeit at a slower pace. Meanwhile, computing demands and energy consumption required by modern artificial intelligence (AI) algorithms have skyrocketed. As an alternative to scaling transistors for general-purpose computing, the integration of transistors with unconventional technologies has emerged as a promising path for domain-specific computing. In this article, we provide a full-stack review of probabilistic computing with p-bits as a representative example of the energy-efficient and domain-specific computing movement. We argue that p-bits could be used to build energy-efficient probabilistic systems, tailored for probabilistic algorithms and applications. From hardware, architecture, and algorithmic perspectives, we outline the main applications of probabilistic computers ranging from probabilistic machine learning and AI to combinatorial optimization and quantum simulation. Combining emerging nanodevices with the existing CMOS ecosystem will lead to probabilistic computers with orders of magnitude improvements in energy efficiency and probabilistic sampling, potentially unlocking previously unexplored regimes for powerful probabilistic algorithms.

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Efficient Probabilistic Computing with Stochastic Perovskite Nickelates

Cited by 14 publications

References 28 publications

Infrared Nanoimaging of Hydrogenated Perovskite Nickelate Memristive Devices

Infrared Nanoimaging of Hydrogenated Perovskite Nickelate Memristive Devices

Proton Conducting Neuromorphic Materials and Devices

A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

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