DESI - Artículos y ponencias sin arbitraje
https://hdl.handle.net/11117/1888
2024-03-29T10:14:20ZPromoting and Empowering the RF and Microwave Community in Costa Rica
https://hdl.handle.net/11117/10668
Promoting and Empowering the RF and Microwave Community in Costa Rica
Rayas-Sánchez, José E.; Chattopadhyay, Goutam
This article describes a recent and very fruitful IEEE MTT-S initiative in Latin America focusing on Costa Rica. Envisaged by the Membership and Geographical Activities (MGA) Committee of the MTT-S Administrative Committee (AdCom), this initiative, which was implemented by the end of 2022, aimed at developing MTT-S membership, volunteer base, and increased engagement in MTT-S activities in Region 9 of IEEE, especially focusing on Costa Rica. The delegation consisting of the MTT-S MGA Chair, Goutam Chattopadhyay, and the MTT-S Region 9 Coordinator, José Rayas-Sánchez, visited San Jose, Costa Rica. The 2022 MTT-S President, Rashaunda Henderson, was to accompany the delegation but had to drop out at the last moment due to some unavoidable circumstances. The delegation carried MTT-S President’s message to the microwave community in Costa Rica. This article presents the rationale and goals of the MTT-S delegation, the planning for the initiative, the agenda and activities, and the outcomes achieved. Several months after its implementation, the results obtained indicate that the initiative was very successful, confirming it as an effective strategy to promote RF and microwave-related technical activities and revitalize communities in similar emerging regions of the world.
2023-11-01T00:00:00ZEqualization Tuning of the PCIe Physical Layer by Using Machine Learning in Industrial Post-silicon Validation
https://hdl.handle.net/11117/9647
Equalization Tuning of the PCIe Physical Layer by Using Machine Learning in Industrial Post-silicon Validation
Rangel-Patiño, Francisco E.; Viveros-Wacher, Andres; Rajyaguru, Chintan; Vega-Ochoa, Edgar A.; Rodriguez-Saenz, Sofia D.; Silva-Cortes, Johana L.; Shival, Hemanth; Rayas-Sánchez, José E.
The increasing complexity of high-speed computer platforms has made post-silicon validation a highly demanding industrial task. A large portion of the circuits to be validated in modern microprocessors corresponds to high-speed input/output (HSIO) links, imposing the need to efficiently tune the transmitter (Tx) and receiver (Rx) equalizers. In this work, we first use unsupervised machine learning techniques to cluster all available post-silicon data from different channels, dividing them into distinct sets of channel conditions. We then develop statistical supervised machine learning models, based on Gaussian process regression (GPR), to predict the eye diagram margins within each data subset. We finally optimize the GPR-based models to obtain the optimal tuning settings for the specific channels. Our proposed method is validated by measurements of the functional eye diagram of an actual industrial computer platform.
2023-06-14T00:00:00ZPCIe Gen5 Physical Layer Equalization Tuning by Using K-means Clustering and Gaussian Process Regression Modeling in Industrial Post-silicon Validation
https://hdl.handle.net/11117/9644
PCIe Gen5 Physical Layer Equalization Tuning by Using K-means Clustering and Gaussian Process Regression Modeling in Industrial Post-silicon Validation
Rangel-Patiño, Francisco E.; Viveros-Wacher, Andres; Rajyaguru, Chintan; Vega-Ochoa, Edgar A.; Rodriguez-Saenz, Sofia D.; Silva-Cortes, Johana L.; Shival, Hemanth; Rayas-Sánchez, José E.
Peripheral component interconnect express (PCIe) is a high-performance interconnect architecture widely adopted in the computer industry. The continuously increasing bandwidth demand from new applications has led to the development of the PCIe Gen5, reaching data rates of 32 GT/s. To mitigate undesired channel effects due to such high-speed, the PCIe specification defines an equalization process at the transmitter (Tx) and the receiver (Rx). Current post-silicon validation practices consist of finding an optimal subset of Tx and Rx coefficients by measuring the eye diagrams across different channels. However, these experiments are very time consuming since they require massive lab measurements. In this paper, we use a K-means approach to cluster all available post-silicon data from different channels and feed those clusters to a Gaussian process regression (GPR)-based metamodel for each channel. We then perform a surrogate-based optimization to obtain the optimal tuning settings for the specific channels. Our methodology is validated by measurements of the functional eye diagram of an industrial computer platform.
2023-06-30T00:00:00ZTC-2 Design Automation Committee—On the Future of RF and Microwave Design Automation—2022
https://hdl.handle.net/11117/8252
TC-2 Design Automation Committee—On the Future of RF and Microwave Design Automation—2022
Gibiino, Gian Piero; Rayas-Sánchez, José E.; Pirola, Marco; Khazaka, Roni; Zhang, Qi-Jun; Root, David E.; Bandler, John W.
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.
2022-10-04T00:00:00Z