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Profil bibliographique

Jabez J. McClelland

Informations fournies par OpenAlex. Research Africa ne déduit ni nationalité, ni poste, ni coordonnées personnelles.

223Publications signalées
4834Citations signalées
3Affiliations récentes

Les institutions déclarées

Les domaines associés

Cold Atom Physics and Bose-Einstein CondensatesElectron and X-Ray Spectroscopy TechniquesAtomic and Molecular PhysicsAtomic and Subatomic Physics ResearchAdvanced Materials Characterization Techniques

Les publications récentes

2026 article OpenAlex

130-nm CMOS-Integrated Superparamagnetic Tunnel Junction-Based p-bit

Ju-Young Yoon, Nuno Caçoilo, Advait Madhavan, Jabez J. McClelland et autres

Probabilistic computers offer promising solutions for computationally hard problems in domains such as combinatorial optimization and machine learning. A key building block in these systems is the probabilistic bit (p-bit), which relies on superparamagnetic tunnel junctions (sMTJs) as its source of randomness. …

jp, us (code pays fourni par la source)

1 citation IEEE Electron Device Letters
Accès ouvert 2026 article OpenAlex

Electron-beam induced modulation of surface conduction in hydrogen-terminated diamond

Sebastian Wood, Evgheni Strelcov, Junyeob Song, Albert Davydov et autres

Hydrogen-terminated diamond is a promising platform for diamond-based high-power-frequency electronics and radiation-hardened semiconductor devices exhibiting controllable electrical conduction through an accumulated sub-surface 2D hole-gas layer. The diamond conductivity is highly sensitive to local surface conditions, offering new opportunities for device engineering, quantum …

gb, us (code pays fourni par la source)

0 citations Diamond and Related Materials
Accès ouvert 2026 preprint OpenAlex

CMOS-integrated superparamagnetic tunnel junction-based p-bit

Ju-Young Yoon, Nuno Caçoilo, Advait Madhavan, Jabez J. McClelland et autres

Probabilistic computers offer promising solutions for computationally hard problems in domains such as combinatorial optimization and machine learning. A key building block in these systems is the probabilistic bit (p-bit), which relies on superparamagnetic tunnel junctions (sMTJs) as its source of randomness. …

0 citations arXiv (Cornell University)
Accès ouvert 2026 preprint OpenAlex

CMOS-integrated superparamagnetic tunnel junction-based p-bit

Ju-Young Yoon, Nuno Caçoilo, Advait Madhavan, Jabez J. McClelland et autres

Probabilistic computers offer promising solutions for computationally hard problems in domains such as combinatorial optimization and machine learning. A key building block in these systems is the probabilistic bit (p-bit), which relies on superparamagnetic tunnel junctions (sMTJs) as its source of randomness. …

jp, us (code pays fourni par la source)

0 citations arXiv (Cornell University)
2025 conference-abstract OpenAlex

Sampling from exponential distributions in the time domain with superparamagnetic tunnel junctions

Temitayo N. Adeyeye, Sidra Gibeault, Daniel Perry Lathrop, Matthew W. Daniels et autres

This work explores time domain statistics of superparamagnetic tunnel junctions (SMTJs). We leverage a temporal encoding scheme to experimentally sample exponential distributions from the dwell time statistics of these devices. We develop SMTJ-based circuits called probabilistic delay cells to produce stochastic time-delayed …

us, fr (code pays fourni par la source)

0 citations
Accès ouvert 2025 article OpenAlex

ReRAM/CMOS Array Integration and Characterization via Design of Experiments

Imtiaz Hossen, William A. Borders, Advait Madhavan, Shweta Joshi et autres

Abstract No two fabricated Resistive Random Access Memory (ReRAM) devices are alike. Each device can have its own individual optimal set of operating parameters that gives the best performance. However, in an array each device needs to be measured in similar operating …

us (code pays fourni par la source)

0 citations Advanced Electronic Materials
Accès ouvert 2025 article OpenAlex

Investigation of key performance metrics in TiOX/TiN based resistive random-access memory cells

Brandon R. Zink, William A. Borders, Advait Madhavan, Brian D. Hoskins et autres

Abstract Resistive random-access memory (RRAM) is a promising beyond-CMOS technology due to its non-volatility, scalability, and high ON/OFF ratio. Furthermore, a single RRAM cell can operate as an analog resistor, meaning that it can be used in more novel computing applications such …

us (code pays fourni par la source)

5 citations Scientific Reports
Accès ouvert 2025 article OpenAlex

Sampling from exponential distributions in the time domain with superparamagnetic tunnel junctions

Temitayo N. Adeyeye, Sidra Gibeault, Daniel Perry Lathrop, Matthew W. Daniels et autres

In the superparamagnetic regime, magnetic tunnel junctions switch between two resistance states due to random thermal fluctuations. The dwell-time distribution in each state is exponential. We sample this distribution using a temporal encoding scheme, in which information is encoded in the time …

us, fr (code pays fourni par la source)

2 citations Physical Review Applied
Accès ouvert 2025 article OpenAlex

Layer ensemble averaging for fault tolerance in memristive neural networks

Osama Yousuf, Brian D. Hoskins, K Ramu, Mitchell Fream et autres

Artificial neural networks have advanced due to scaling dimensions, but conventional computing struggles with inefficiencies due to memory bottlenecks. In-memory computing architectures using memristor devices offer promise but face challenges due to hardware non-idealities. This work proposes layer ensemble averaging—a hardware-oriented fault …

us (code pays fourni par la source)

19 citations Nature Communications
Accès ouvert 2024 preprint OpenAlex

Sampling from exponential distributions in the time domain with superparamagnetic tunnel junctions

Temitayo N. Adeyeye, Sidra Gibeault, Daniel Perry Lathrop, Matthew W. Daniels et autres

In the superparamagnetic regime, magnetic tunnel junctions switch between two resistance states due to random thermal fluctuations. The dwell time distribution in each state is exponential. We sample this distribution using a temporal encoding scheme, in which information is encoded in the …

0 citations arXiv (Cornell University)

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