In this paper, a Gaussian-process surrogate modeling methodology is used to accurately and efficiently model transformers, which are still a bottleneck in radio-frequency and millimeter-wave circuit design. The proposed model is useful for a wide range of frequencies from DC up to the millimeter-wave range (over 100 GHz). The technique is statistically validated against full-wave electromagnetic simulations. The efficient model evaluation enables its exploitation in iterative user-driven design approaches, as well as automated design exploration involving thousands of simulations. As experimental results, the model is used in several scenarios, such as the design of an inter-stage amplifier operating at 60 GHz, where the model assisted in the simulation of the transformers and baluns used, and the design of individual transformers and a matching network.

Journal Paper - AEU - International Journal of Electronics and Communications, vol. 128, article 153496, 2021 ELSEVIER
DOI: 10.1016/j.aeue.2020.153496    ISSN: 1434-8411    » doi

Porous titanium is a metallic biomaterial with good properties for the clinical repair of cortical bone tissue, although the presence of pores can compromise its mechanical behavior and clinical use. It is therefore necessary to characterize the implant pore size and distribution in a suitable way. In this work, we explore the new use of electrical impedance spectroscopy for the characterization and monitoring of titanium bone implants. Electrical impedance spectroscopy has been used as a non-invasive route to characterize the volumetric porosity percentage (30%, 40%, 50% and 60%) and the range of pore size (100-200 and 355-500 mm) of porous titanium samples obtained with the space-holder technique. Impedance spectroscopy is proved to be an appropriate technique to characterize the level of porosity of the titanium samples and pore size, in an affordable and non-invasive way. The technique could also be used in smart implants to detect changes in the service life of the material, such as the appearance of fractures, the adhesion of osteoblasts and bacteria, or the formation of bone tissue.

Journal Paper - Sensors, vol. 20, no. 19, article 4358, 2020 MDPI
DOI: 10.3390/s20164358    ISSN: 1424-8220    » doi

Convolutional Neural Networks (ConvNets) are directed acyclic graphs with node transitions determined by a set of configuration parameters. In this paper, we describe a dynamically configurable hardware architecture that enables data allocation strategy adjustment according to ConvNets layer characteristics. The proposed flexible scheduling solution allows the accelerator design to be portable across various scenarios of computation and memory resources availability. For instance, FPGA block-RAM resources can be properly balanced for optimization of data distribution and minimization of off-chip memory accesses. We explore the selection of tailored scheduling policies that translate into efficient on-chip data reuse and hence lower energy consumption. The system can autonomously adapt its behavior with no need of platform reconfiguration nor user supervision. Experimental results are presented and compared with state-of-the-art accelerators.

Conference - IEEE International Symposium on Circuits and Systems ISCAS 2020

Three-dimensional printing technologies have been recently proposed to monitor cell cultures and implement cell bioreactors for different biological applications. In tissue engineering, the control of tissue formation is crucial to form tissue constructs of clinical relevance, and 3D printing technologies can also play an important role for this purpose. In this work, we study 3D-printed sensors that have been recently used in cell culture and tissue engineering applications in biological laboratories, with a special focus on the technique of electrical impedance spectroscopy. Furthermore, we study new 3D-printed actuators used for the stimulation of stem cells cultures, which is of high importance in the process of tissue formation and regenerative medicine. Key challenges and open issues, such as the use of 3D printing techniques in implantable devices for regenerative medicine, are also discussed.

Journal Paper - Sensors, vol. 20, no. 19, article 5617, 2020 MDPI
DOI: 10.3390/s20195617    ISSN: 1424-8220    » doi

This paper proposes a novel yield-aware optimization methodology that can be used for mixed-domain synthesis of robust micro-electro-mechanical systems (MEMS). The robust Pareto front optimization of a MEMS accelerometer system, which includes a capacitive MEMS sensor and an analog read-out circuitry, is realized by co-optimization of the mixed-domain system where the sensor performances are evaluated using highly accurate analytical models and the circuit level simulations are carried out by an electrical simulator. Two different approaches for yield-aware optimization have been implemented in the synthesis loop. The Quasi Monte Carlo (QMC) technique has been used to embed the variation effects into the optimization loop. The results for both two- and three-dimensional yield-aware optimization are quite promising for robust MEMS accelerometer synthesis.

Journal Paper - Microelectronics Journal, vol. 103, article 104876, 2020 ELSEVIER
DOI: 10.1016/j.mejo.2020.104876    ISSN: 0026-2692    » doi