| dc.contributor.author | Gibiino, Gian Piero | |
| dc.contributor.author | Rayas-Sánchez, José E. | |
| dc.contributor.author | Pirola, Marco | |
| dc.contributor.author | Khazaka, Roni | |
| dc.contributor.author | Zhang, Qi-Jun | |
| dc.contributor.author | Root, David E. | |
| dc.contributor.author | Bandler, John W. | |
| dc.date.accessioned | 2022-10-21T22:00:10Z | |
| dc.date.accessioned | 2023-03-21T16:19:21Z | |
| dc.date.available | 2022-10-21T22:00:10Z | |
| dc.date.available | 2023-03-21T16:19:21Z | |
| dc.date.issued | 2022-10-04 | |
| dc.identifier.citation | G. P. Gibiino, J. E. Rayas-Sánchez, J. B. King, M. Pirola, R. Khazaka, Q. J. Zhang, D. E. Root, and J. W. Bandler, “TC-2 Design Automation Committee—On the future of RF and microwave design automation—2022,” IEEE Microwave Magazine, vol. 23, no. 11, pp. 104-105, Nov. 2022. DOI: 10.1109/MMM.2022.3196416 | es_MX |
| dc.identifier.issn | 1527-3342 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12032/73470 | |
| dc.description | TC-2 Design Automation Committee (formerly MTT-1 CAD), established in 1968, focuses on advances in all aspects of methods, software, and technologies for the modeling, simulation, and design optimization of high-frequency circuits and systems. From radio frequency to terahertz, engineering innovation hinges on the availability of state-of-the-art modeling techniques and design automation methods capable to handle new mathematical representations and design methodologies, as well as novel manufacturing processes and materials. Here, we venture on the future of RF and microwave design automation within the next decade. | es_MX |
| dc.description.sponsorship | ITESO, A.C. | es |
| dc.language.iso | eng | es_MX |
| dc.publisher | IEEE | es_MX |
| dc.rights.uri | http://quijote.biblio.iteso.mx/licencias/CC-BY-NC-ND-2.5-MX.pdf | es_MX |
| dc.subject | microwave, RF, design automation, future, Bayesian methods, deep neural networks, reinforcement learning, cognition space mapping, confined surrogates, digital twins | es_MX |
| dc.subject | Microwave | es_MX |
| dc.subject | RF | es_MX |
| dc.subject | Design Automation | es_MX |
| dc.subject | Bayesian Methods | es_MX |
| dc.subject | Deep Neural Networks | es_MX |
| dc.subject | Reinforcement Learning | es_MX |
| dc.subject | Cognition Space Mapping | es_MX |
| dc.subject | Confined Surrogates | es_MX |
| dc.subject | Digital Twins | es_MX |
| dc.title | TC-2 Design Automation Committee—On the Future of RF and Microwave Design Automation—2022 | es_MX |
| dc.type | info:eu-repo/semantics/article | es_MX |
