E. Miranda

7.4k total citations
282 papers, 4.7k citations indexed

About

E. Miranda is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience and Materials Chemistry. According to data from OpenAlex, E. Miranda has authored 282 papers receiving a total of 4.7k indexed citations (citations by other indexed papers that have themselves been cited), including 270 papers in Electrical and Electronic Engineering, 54 papers in Cellular and Molecular Neuroscience and 38 papers in Materials Chemistry. Recurrent topics in E. Miranda's work include Advanced Memory and Neural Computing (168 papers), Semiconductor materials and devices (162 papers) and Ferroelectric and Negative Capacitance Devices (124 papers). E. Miranda is often cited by papers focused on Advanced Memory and Neural Computing (168 papers), Semiconductor materials and devices (162 papers) and Ferroelectric and Negative Capacitance Devices (124 papers). E. Miranda collaborates with scholars based in Spain, Argentina and Italy. E. Miranda's co-authors include J. Suñé, M. Nafrı́a, X. Aymerich, David Jiménez, Ming Liu, L. Perniola, Christian Wenger, C. Cagli, Xiaojuan Lian and R. Rodrı́guez and has published in prestigious journals such as Advanced Materials, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

E. Miranda

259 papers receiving 4.7k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
E. Miranda 4.6k 1.2k 986 531 291 282 4.7k
L. Perniola 2.9k 0.6× 707 0.6× 855 0.9× 407 0.8× 266 0.9× 138 3.0k
Byoungil Lee 3.6k 0.8× 1.2k 1.0× 758 0.8× 709 1.3× 271 0.9× 19 3.7k
Xiangshui Miao 2.8k 0.6× 1.1k 0.9× 972 1.0× 432 0.8× 494 1.7× 226 3.4k
Writam Banerjee 2.9k 0.6× 959 0.8× 740 0.8× 721 1.4× 185 0.6× 68 3.1k
Michael N. Kozicki 4.7k 1.0× 1.6k 1.3× 1.3k 1.3× 1.2k 2.3× 263 0.9× 152 5.0k
L. Goux 3.2k 0.7× 651 0.6× 1.1k 1.1× 655 1.2× 94 0.3× 149 3.4k
In-Kyeong Yoo 3.9k 0.8× 968 0.8× 1.7k 1.7× 1.1k 2.0× 148 0.5× 28 4.4k
B. Govoreanu 4.1k 0.9× 610 0.5× 1.1k 1.2× 575 1.1× 92 0.3× 169 4.3k
Saumil Joshi 3.3k 0.7× 1.6k 1.4× 446 0.5× 549 1.0× 606 2.1× 22 3.4k
Rakesh Jeyasingh 2.4k 0.5× 999 0.9× 751 0.8× 456 0.9× 373 1.3× 32 2.6k

Countries citing papers authored by E. Miranda

Since Specialization
Citations

This map shows the geographic impact of E. Miranda'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 E. Miranda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites E. Miranda more than expected).

Fields of papers citing papers by E. Miranda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by E. Miranda. 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 E. Miranda. The network helps show where E. Miranda may publish in the future.

Co-authorship network of co-authors of E. Miranda

This figure shows the co-authorship network connecting the top 25 collaborators of E. Miranda. A scholar is included among the top collaborators of E. Miranda 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 E. Miranda. E. Miranda 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
1.
Miranda, E., et al.. (2026). Trap-Controlled Conduction and Metal–Insulator Transition in Superconducting Cuprate Memristors. ACS Applied Electronic Materials. 8(3). 1099–1107.
2.
Miranda, E., Eszter Piros, Tobias Vogel, et al.. (2025). Assessing electric-field engineering of filamentary-type conduction in Cu/HfO2/Pt memristive devices using the quantum point-contact model. Applied Physics Letters. 126(21). 2 indexed citations
3.
Balcells, Ll., et al.. (2025). Field‐Induced Phase Transitions in Cuprate Superconductors for Cryogenic in‐Memory Computing. Small. 21(14). e2411908–e2411908. 2 indexed citations
4.
Milano, Gianluca, Luca Boarino, Luca Callegaro, et al.. (2025). A quantum resistance memristor for an intrinsically traceable International System of Units standard. Nature Nanotechnology. 20(12). 1884–1890. 1 indexed citations
5.
6.
Suñé, J., et al.. (2024). Event-Driven Stochastic Compact Model for Resistive Switching Devices. IEEE Transactions on Electron Devices. 71(8). 4649–4654. 3 indexed citations
7.
Vicario, José López, et al.. (2024). Memristor Crossbar Array Simulation for Deep Learning Applications. IEEE Transactions on Nanotechnology. 23. 512–515.
8.
Rodrı́guez, R., E. Miranda, J. Martín-Martínez, et al.. (2024). Noise-Induced Homeostasis in Memristor-Based Neuromorphic Systems. IEEE Electron Device Letters. 45(8). 1524–1527. 3 indexed citations
9.
Rodrı́guez, R., E. Miranda, J. Martín-Martínez, et al.. (2024). Stochastic Resonance in HfO₂-Based Memristors: Impact of External Noise on the Binary STDP Protocol. IEEE Transactions on Electron Devices. 71(9). 5761–5766.
10.
Miranda, E., et al.. (2023). SPICE Simulation of Quantum Transport in Al 2 O 3 /HfO 2 -Based Antifuse Memory Cells. IEEE Electron Device Letters. 44(7). 1180–1183. 1 indexed citations
11.
García, H., Fernando Aguirre, Mireia Bargalló González, et al.. (2023). Effects of the voltage ramp rate on the conduction characteristics of HfO2-based resistive switching devices. Journal of Physics D Applied Physics. 56(36). 365108–365108. 8 indexed citations
12.
Aguirre, Fernando, Eszter Piros, Nico Kaiser, et al.. (2023). Simulation of the effect of material properties on yttrium oxide memristor-based artificial neural networks. SHILAP Revista de lepidopterología. 1(3). 1 indexed citations
13.
Milano, Gianluca, Masakazu Aono, Luca Boarino, et al.. (2022). Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications. Advanced Materials. 34(32). e2201248–e2201248. 68 indexed citations
14.
Roldán, J.B., Rodrigo Picos, E. Miranda, et al.. (2021). On the Thermal Models for Resistive Random Access Memory Circuit Simulation. Nanomaterials. 11(5). 1261–1261. 45 indexed citations
15.
Petzold, Stefan, Eszter Piros, Alexander Zintler, et al.. (2020). Tailoring the Switching Dynamics in Yttrium Oxide‐Based RRAM Devices by Oxygen Engineering: From Digital to Multi‐Level Quantization toward Analog Switching. Advanced Electronic Materials. 6(11). 30 indexed citations
16.
Calixto, Manuel, D. Maldonado, E. Miranda, & J.B. Roldán. (2020). Modeling of the temperature effects in filamentary-type resistive switching memories using quantum point-contact theory. Journal of Physics D Applied Physics. 53(29). 295106–295106. 7 indexed citations
17.
Petzold, Stefan, E. Miranda, S. U. Sharath, et al.. (2019). Analysis and simulation of the multiple resistive switching modes occurring in HfOx-based resistive random access memories using memdiodes. Journal of Applied Physics. 125(23). 24 indexed citations
18.
Castán, Helena, S. Dueñas, H. García, et al.. (2018). Analysis and control of the intermediate memory states of RRAM devices by means of admittance parameters. Journal of Applied Physics. 124(15). 14 indexed citations
19.
Pan, Chengbin, Yanfeng Ji, Na Xiao, et al.. (2017). Coexistence of Grain‐Boundaries‐Assisted Bipolar and Threshold Resistive Switching in Multilayer Hexagonal Boron Nitride. Advanced Functional Materials. 27(10). 276 indexed citations
20.
Li, Yu, Meiyun Zhang, Shibing Long, et al.. (2017). Investigation on the Conductive Filament Growth Dynamics in Resistive Switching Memory via a Universal Monte Carlo Simulator. Scientific Reports. 7(1). 11204–11204. 30 indexed citations

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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