Browsing by Author "Guilherme, Jorge"
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- Accomplishing PROMISE, PROgrammable MIxed Signal ASIC Electronics FrameworkPublication . Berrojo, Luis; Veljkovic, Filip; Ayzac, Philippe; Maggioni-Mezzomo, Cecilia; Trouche, Christophe; Kakoulin, Michael; Franciscatto, Giancarlo; Berti, Laurent; Arslanov, Dmitry; Póvoa, Ricardo; Guilherme, Jorge; Roumkou, Anna; Tukkiniemi, KariThis paper presents the activities, current results and status of the PROMISE (PROgrammable MIxed Signal Electronics) project which started at the beginning of 2020 . The project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 870358. It’s tailored to bring to the space community a flexible, cost competitive mixed-signal ASIC architecture design ecosystem built on a portfolio of silicon qualified hardened IP blocks. It includes analogue IPs such as ADC, DAC, PLL, LDO, BG, LO, POR and HV MOS transistors. A digital embedded FPGA core provides a flexible programmable element and an NVM permits reconfiguration abilities without the need of using an external memory. All these elements are included on the PILOT Circuit ASIC that will be submitted to electrical validation and radiation testing as part of the qualification process of these basic elements for their future use as building blocks.
- FUZYE: A Fuzzy C-Means Analog IC Yield Optimization using Evolutionary-based AlgorithmsPublication . Canelas, António; Póvoa, Ricardo; Martins, Ricardo; Lourenço, Nuno; Guilherme, Jorge; Carvalho, João Paulo; Horta, NunoThis paper presents fuzzy c-means-based yield estimation (FUZYE), a methodology that reduces the time impact caused by Monte Carlo (MC) simulations in the context of analog integrated circuits (ICs) yield estimation, enabling it for yield optimization with population-based algorithms, e.g., the genetic algorithm (GA). MC analysis is the most general and reliable technique for yield estimation, yet the considerable amount of time it requires has discouraged its adoption in population-based optimization tools. The proposed methodology reduces the total number of MC simulations that are required, since, at each GA generation, the population is clustered using a fuzzy c-means (FCMs) technique, and, only the representative individual (RI) from each cluster is subject to MC simulations. This paper shows that the yield for the rest of the population can be estimated based on the membership degree of FCM and RIs yield values alone. This new method was applied on two real circuit-sizing optimization problems and the obtained results were compared to the exhaustive approach, where all individuals of the population are subject to MC analysis. The FCM approach presents a reduction of 89% in the total number of MC simulations, when compared to the exhaustive MC analysis over the full population. Moreover, a k-means-based clustering algorithm was also tested and compared with the proposed FUZYE, with the latest showing an improvement up to 13% in yield estimation accuracy