Nano-oscillators based on phase transitions materials (PTM) are being explored for the implementation of different non-conventional computing paradigms. This paper describes the capability of such autonomous non-linear oscillators to store phase-encoded information. A latch based in sub-harmonic injection locking using an oscillator composed of a PTM device and a transistor is described. Resistive coupling is used to inject both a required synchronization signal and the input to be stored. Operation of the proposed latch implementation, the embedding of functionality into the latch and its application to frequency division are illustrated and validated by simulation.

Journal Paper - IEEE Transactions on Nanotechnology, vol. 19, pp 107-112, 2020 IEEE
DOI: 10.1109/TNANO.2020.2965243    ISSN: 1536-125X    » doi

Dear Readers, It is a great honor and privilege for me to start my two-year term of duty as Editor-in-Chief (EiC) of IEEE Transactions on Circuits and Systems - Part II: Express Briefs (TCAS-II) and I am very thankful to the IEEE Circuits and Systems Society (CASS) for giving me this opportunity. During the last four years, I have been very fortunate to work as Deputy Editor-in-Chief (DEiC) of TCAS-II together with Professor C.K. Michael Tse, who has set the bar very high for me! I would like to begin this first issue of TCAS-II in 2020 by expressing my most sincere and warm gratitude to Professor Tse for his great job, dedication, and guidance during all this time working together. I learned a lot from him. Thank you so much, Michael!

Journal Paper - IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 67, no.1, pp 1-3, 2020 IEEE
DOI: 10.1109/TCSII.2019.2956327    ISSN: 1549-7747    » doi

Millimeter-wave circuits in current nanometric technologies are especially sensitive to process variations, which can seriously degrade the device behavior and reduce fabrication yield. To tackle this issue, conservative designs and large design margins are widely used solutions. Another approach consists in introducing variable elements, also called tuning knobs, to allow post-fabrication tuning. One-shot statistical calibration techniques take advantage of advanced machine learning regression tools to propose a set of tuning knobs values that enhance the circuit performance based on simple measurements. Training the regression models require a huge amount of data covering the device performances, the effect of the tuning knobs and the simple measurements that guide the regression. In this work, we propose an efficient method for generating such a data set that reduces noticeably the size of the required training set for an accurate calibration.

Conference - Int. Conf. on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design SMACD 2019

One of the biggest problems that society is currently facing is violence against women. In recent years, tangible progress in protecting and saving the lives of female victims of intimate partner/family-related homicide has not been made, so targeted responses are clearly needed. In this work, an electronic device to help victims of gender-based violence who live with their aggressor has been designed. The system is built on Bluetooth Low Energy technology allowing a wireless communication between device and mobile phone with a low power consumption. The device is controlled by three different commands and is capable of sending messages through a mobile phone to a Control Center. Depending on the nature of the received messages, the Control Center will take the appropriate measures to assist the victim. The design has been made paying special attention to a reduced size so that the device can easily be camouflaged in any accessory of the victim's jewelry, thus going unperceived to the possible aggressor.

Conference - Multidisciplinary International Conference of Research Applied to Defense and Security MICRADS 2019

In this paper, the use of machine learning techniques to repurpose already available Pareto optimal fronts of analog integrated circuit blocks for new contexts (loads, supply voltage, etc.) is explored. Data from previously sized circuits is used to train models that predict both circuit performance under the new context and the corresponding device sizes. A two-model chain is proposed, where, in the first layer, a multivariate polynomial regression estimates the performance tradeoffs. The output of this performance model is then used as input of an artificial neural network that predicts the device sizing that corresponds to that performance. Moreover, the models are trained with optimized sizing solutions, leading almost instantly to predicted solutions that are near optimal for the new context. The proposed methodology was integrated into a new framework and tested against a real circuit topology, with promising results. The model was able to predict wider and, in some cases, better, performance tradeoff, when compared to independent optimization runs for the same context, despite requiring 400 times fewer circuit simulations.

Conference - Int. Conf. on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design SMACD 2019