Rashmi Jha

1.1k total citations
93 papers, 772 citations indexed

About

Rashmi Jha is a scholar working on Electrical and Electronic Engineering, Cognitive Neuroscience and Materials Chemistry. According to data from OpenAlex, Rashmi Jha has authored 93 papers receiving a total of 772 indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Electrical and Electronic Engineering, 14 papers in Cognitive Neuroscience and 13 papers in Materials Chemistry. Recurrent topics in Rashmi Jha's work include Advanced Memory and Neural Computing (52 papers), Ferroelectric and Negative Capacitance Devices (42 papers) and Semiconductor materials and devices (33 papers). Rashmi Jha is often cited by papers focused on Advanced Memory and Neural Computing (52 papers), Ferroelectric and Negative Capacitance Devices (42 papers) and Semiconductor materials and devices (33 papers). Rashmi Jha collaborates with scholars based in United States, Australia and India. Rashmi Jha's co-authors include Yibo Li, Veena Misra, Saptarshi Mandal, Bipin Rajendran, Rino Choi, Kevin Leedy, Jehee Lee, Alexander M. Rush, Wenbo Chen and Swarup Bhunia and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and PLoS ONE.

In The Last Decade

Rashmi Jha

83 papers receiving 748 citations

Peers

Rashmi Jha
Takumi Mikawa Japan
M. Bocquet France
Yumin Kim South Korea
Frederick T. Chen Taiwan
Sebastián Pazos Argentina
Furqan Zahoor India
Shiva Subbulakshmi Radhakrishnan United States
N. Castellani France
Siyan Lin China
Takumi Mikawa Japan
Rashmi Jha
Citations per year, relative to Rashmi Jha Rashmi Jha (= 1×) peers Takumi Mikawa

Countries citing papers authored by Rashmi Jha

Since Specialization
Citations

This map shows the geographic impact of Rashmi Jha's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rashmi Jha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rashmi Jha more than expected).

Fields of papers citing papers by Rashmi Jha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rashmi Jha. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rashmi Jha. The network helps show where Rashmi Jha may publish in the future.

Co-authorship network of co-authors of Rashmi Jha

This figure shows the co-authorship network connecting the top 25 collaborators of Rashmi Jha. A scholar is included among the top collaborators of Rashmi Jha based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rashmi Jha. Rashmi Jha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Emmert, John M., et al.. (2024). A Survey of Electromagnetic Radiation Based Hardware Assurance and Reliability Monitoring Methods in Integrated Circuits. IEEE Access. 12. 150623–150638. 2 indexed citations
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Jha, Rashmi, et al.. (2024). On-Chip Antennas for IC Reliability and Aging Monitoring. 1276–1280. 1 indexed citations
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Nikodemski, Stefan, et al.. (2024). Electrical Characterization of AlScN Ferroelectrics. 257–261. 1 indexed citations
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Jha, Rashmi, et al.. (2023). Clustering RF Signals with the Growing Self-Organizing Map for Dynamic Spectrum Access. 249–253. 1 indexed citations
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Barve, Sunita, et al.. (2023). Power-Efficient Clustering Using Programmable VT FETs in Neuromorphic Architectures. 1–4. 1 indexed citations
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Jha, Rashmi, et al.. (2022). Exploring the associative learning capabilities of the segmented attractor network for lifelong learning. Frontiers in Artificial Intelligence. 5. 910407–910407. 1 indexed citations
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Lu, Ming‐Lun, et al.. (2021). A deep learning approach for lower back-pain risk prediction during manual lifting. PLoS ONE. 16(2). e0247162–e0247162. 16 indexed citations
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Jha, Rashmi, et al.. (2021). NeuroSOFM: A Neuromorphic Self-Organizing Feature Map Heterogeneously Integrating RRAM and FeFET. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits. 7(2). 97–105. 8 indexed citations
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Ghosh, Swaroop, et al.. (2018). Design Space Exploration for Selector Diode-STTRAM Crossbar Arrays. IEEE Transactions on Magnetics. 54(6). 1–5. 3 indexed citations
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Sarim, Mohammad, Manish Kumar, Rashmi Jha, & Ali A. Minai. (2017). Memristive device based learning for navigation in robots. Bioinspiration & Biomimetics. 12(6). 66011–66011. 3 indexed citations
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Sarim, Mohammad, et al.. (2016). An Artificial Brain Mechanism to Develop a Learning Paradigm for Robot Navigation. 4 indexed citations
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Mandal, Saptarshi, et al.. (2014). Novel synaptic memory device for neuromorphic computing. Scientific Reports. 4(1). 5333–5333. 86 indexed citations
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Li, Yibo, et al.. (2012). Switching dynamics and charge transport studies of resistive random access memory devices. Applied Physics Letters. 101(11). 32 indexed citations
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Li, Yibo, et al.. (2012). Switching Characteristics of $\hbox{Ru/HfO}_{2} \hbox{/TiO}_{2-x}\hbox{/Ru}$ RRAM Devices for Digital and Analog Nonvolatile Memory Applications. IEEE Electron Device Letters. 33(5). 706–708. 52 indexed citations
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Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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