Demo Data from EDBT 2026 / reviewers in the wild / expert
Pedro Rosales-Quintero
dblp:252/1438
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-8352-5114ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Integrated circuit design · 87% Electronic design automation · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Integrated circuit design › semiconductor device modeling
compact modeling |
0.6 | 1 | 2022 | A Compact Modeling Methodology for Experimental Memristive Devices · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2022 |
Integrated circuit design › memristor
memristor device modeling |
0.6 | 1 | 2022 | A Compact Modeling Methodology for Experimental Memristive Devices · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2022 |
Electronic design automation
circuit simulation |
0.2 | 1 | 2022 | A Compact Modeling Methodology for Experimental Memristive Devices · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2022 |
Methods — techniques the papers use, named apart from their topics
uniform design · 0.6mapping distance · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | A Compact Modeling Methodology for Experimental Memristive DevicesabstractA methodology to create and develop electrical compact models for memristive devices is presented. It comprises a six-step algorithm that uses experimental data of memristive devices in order to obtain a compact model suitable for circuit-level simulations. The model methodology makes use of the definition of the intrinsic variables of memristive systems, to obtain the state map that describes the system, from which the mathematical expressions of the model are obtained. In order to illustrate the usage of the algorithm, a compact memristive model is developed by using experimental data from a TiO2-based resistive switching (RS) device. The resulting mathematical model is compared to the experimental data by computing the mean absolute percentage error (MAPE) between both, which results in 11%, and reduces to 6% after a piecewise approach. The circuit-level representation is implemented using a SPICE macromodel and a Verilog-A module, from which the memristive behavior is also properly reproduced. Jesús Jiménez-León, Arturo Sarmiento-Reyes, Pedro Rosales-Quintero |
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. | 3 |