Integrated Sensing Architectures and Tools for Health Care

This project addresses high-risk, high-reward research of integrated sensing and computing architectures, as well as of models, methods and tools for their design and operation. Such architectures provide the bridge between bio-systems and information processing systems, where a bio-system is an abstraction of a human in terms of biophysical parameters. Breakthroughs in data acquisition, processing and decision making support will enable new smart-health applications.
The outcome of this research will have a deep and broad impact on health care, because it will improve diagnosis and therapy in a variety of cases. Namely, it will boost the quality and quantity of the acquired biophysical data, possibly in real time, by leveraging multiple sensing modalities and dedicated computing architectures. The use of formal methods for design, data evaluation and decision making support will enhance the quality of the diagnostic platforms and will ease their qualification and adoption. Moreover, the integration of sensing and electronics and their in-field programmability will reduce production cost and lower the barrier of adoption, thus providing for better and more affordable health care means.

The overall goal of this project is to achieve a multiple sensing platform, the related readout and decision mechanisms and its validation. Thus our priority has been to analyze the sensing and computing primitives in the first part of the project.
In the sensing area, we studied the physical properties of Silicon NanoWire-based sensors and we validated the experimental data with an analytical model capturing the nonlinear behavior in presence of analytes. These devices and models are applicable to measuring very small quantities of breast-cancer biomarkers, and thus are promising for early detection of the disease. A second study was applied to transistors with extended gates, that are functionalized by aptamers, and that can be used to detect analytes in solution. Both approaches are useful in determining the viable and optimal structures to create a semiconductor-based platform to perform concurrent analyses. Next, we have used nanostructures as electrodes to measure ions in body fluids, such as sweat. In particular, we have designed a very precise Lithium sensor that can be eventually integrated in an arm or head band, and thus be worn continuously for personal health monitoring.

In the data processing domain domain, we investigated hierarchical logic design of decision circuits based on majority logic. Hierarchical logic synthesis can be achieved by functional decomposition of large logic functions into look-up tables (LUTs), and in the search for optimal design. Within this general paradigm we have extended the notion of Majority Inverter Graphs (MIGs) and we have shown how LUT mapping and exact LUT decomposition can be used to create an MIG optimization method. In a second phase, we have addressed the question of how large are circuits that can be designed in an optimum way. These methods, called exact methods, address both the design of both minimum delay and area circuits. We have shown to be able to achieve the optimum for circuits up to six inputs. We have demonstrated as well that large circuits can be decomposed into 6-input LUTs that can be optimized and that this overall procedure yields circuits that are better than those achieved by traditional means. We have also researched various ways of optimizing logic circuits for particular emerging technologies.

The integrated biosensor prototypes are unique in their kind, as they use novel materials and structures. Thus they can eventually provide us with the primitives to realize programmable multi-sensors, thus enabling lower-cost diagnostics and therapy.
The computational models for the design of decision circuits exploit the novel properties of majority functions, enabling the optimization of circuits through algebraic transformations. This model, and the related software design tools, will enable us to design complex circuitry with smaller delay, power consumption and/or area. Overall these circuits will complement the sensing structure by elaborating the information and thus supporting transparent readout for medical practitioners.

Periodic Reporting for period 4 - CyberCare (Integrated Sensing Architectures and Tools for Health Care)

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