Smart Embedded Systems for Biomedical Applications

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Organic Bioelectronics Development in Italy: A Review

In recent years, studies concerning Organic Bioelectronics have had a constant growth due to the interest in disciplines such as medicine, biology and food safety in connecting the digital world with the biological one. Specific interests can be found in organic neuromorphic devices and organic transistor sensors, which are rapidly growing due to their low cost, high sensitivity and biocompatibility. This trend is evident in the literature produced in Italy, which is full of breakthrough papers concerning organic transistors-based sensors and organic neuromorphic devices. Therefore, this review focuses on analyzing the Italian production in this field, its trend and possible future evolutions

Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications

In the last decade a large scientific community has focused on the study of the memristor. The memristor is thought to be by many the best alternative to CMOS technology, which is gradually showing its flaws. Transistor technology has developed fast both under a research and an industrial point of view, reducing the size of its elements to the nano-scale. It has been possible to generate more and more complex machinery and to communicate with that same machinery thanks to the development of programming languages based on combinations of boolean operands. Alas as shown by Moore’s law, the steep curve of implementation and of development of CMOS is gradually reaching a plateau. It is clear the need of studying new elements that can combine the efficiency of transistors and at the same time increase the complexity of the operations. Memristors can be described as non-linear resistors capable of maintaining memory of the resistance state that they reached. From their first theoretical treatment by Professor Leon O. Chua in 1971, different research groups have devoted their expertise in studying the both the fabrication and the implementation of this new promising technology. In the following thesis a complete study on memristors and memristive elements is presented. The road map that characterizes this study departs from a deep understanding of the physics that govern memristors, focusing on the HP model by Dr. Stanley Williams. Other devices such as phase change memories (PCMs) and memristive biosensors made with Si nano-wires have been studied, developing emulators and equivalent circuitry, in order to describe their complex dynamics. This part sets the first milestone of a pathway that passes trough more complex implementations such as neuromorphic systems and neural networks based on memristors proving their computing efficiency. Finally it will be presented a memristror-based technology, covered by patent, demonstrating its efficacy for clinical applications. The presented system has been designed for detecting and assessing automatically chronic wounds, a syndrome that affects roughly 2% of the world population, through a Cellular Automaton which analyzes and processes digital images of ulcers. Thanks to its precision in measuring the lesions the proposed solution promises not only to increase healing rates, but also to prevent the worsening of the wounds that usually lead to amputation and death

Organic electronics for neuromorphic computing

Neuromorphic computing could address the inherent limitations of conventional silicon technology in dedicated machine learning applications. Recent work on silicon-based asynchronous spiking neural networks and large crossbar-arrays of two-terminal memristive devices has led to the development of promising neuromorphic systems. However, delivering a compact and efficient parallel computing technology, such as artificial neural networks embedded in hardware, remains a significant challenge. Organic electronic materials offer an attractive alternative for such systems and could provide biocompatible and relatively inexpensive neuromorphic devices with low-energy switching and excellent tunability. Here, we review the development of organic neuromorphic devices. We consider different resistance switching mechanisms, which typically rely on electrochemical doping or charge trapping, and discuss the challenges the field faces in implementing low power neuromorphic computing, which include device downscaling, improving device speed, state retention and array compatibility. We highlight early demonstrations of device integration into arrays and finally consider future directions and potential applications of this technology

A Study of Biomedical Sensors Based on Layered Semiconductors: From Characteristics to Nanofabrication Approaches

Among other layered two-dimensional (2D) materials, transition metal dichalcogenides (TMDCs) have revealed their importance in developing novel electronic devices such as field-effect transistors (FETs), optoelectronics, and biomedical sensors. The superior electrical, mechanical, and optoelectronic characteristics, in combination with naturally formed sizable and tunable bandgap of TMDCs, have turned out to be promising for making new biomedical sensors. Despite such a bright prospect, there remain critical scientific and technical gaps that should be filled to enable advanced and practical biomedical sensor applications. Specifically, such gaps include (i) loss of operation stability of MoS2 FET biosensors under wet conditions, (ii) lack of reusability of the electronic biosensors made of TMDCs, and (iii) absence of scalable nanofabrication methods capable of producing well-defined TMDC device patterns. A series of studies presented in this thesis leveraged scientific and technical knowledge to deal with the aforementioned urgent demands and was categorized into three main topics: (i) devise a cycle-wise method for operating MoS2 FET biosensors integrated with a microfluidic channel, which alleviates the liquid-solution-induced issues; (ii) design a new biosensor structure consisting of a bio-tunable nanoplasmonic window and a low-noise few-layer MoS2 photodetector, which can enable highly sensitive, fast, and reusable biosensing processes; (iii) invent scalable nanofabrication and nanomanufacturing approaches capable of producing orderly-arranged TMDCs device channel patterns at designated locations on a target substrate. The presented works have engineered layered semiconductors and device structures based on the scientific knowledge and device physics to realize practical and functional TMDC-based biomedical devices. Additionally, the nanofabrication methods invented in this thesis work could be further developed into cost-efficient and scalable nanomanufacturing techniques that will speed up the development of a wide variety of new device applications made of layered semiconductors.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163205/1/bhryu_1.pd

Biosensors for Biomolecular Computing: a Review and Future Perspectives

Biomolecular computing is the field of engineering where computation, storage, communication, and coding are obtained by exploiting interactions between biomolecules, especially DNA, RNA, and enzymes. They are a promising solution in a long-term vision, bringing huge parallelism and negligible power consumption. Despite significant efforts in taking advantage of the massive computational power of biomolecules, many issues are still open along the way for considering biomolecular circuits as an alternative or a complement to competing with complementary metal–oxide–semiconductor (CMOS) architectures. According to the Von Neumann architecture, computing systems are composed of a central processing unit, a storage unit, and input and output (I/O). I/O operations are crucial to drive and read the computing core and to interface it to other devices. In emerging technologies, the complexity overhead and the bottleneck of I/O systems are usually limiting factors. While computing units and memories based on biomolecular systems have been successfully presented in literature, the published I/O operations are still based on laboratory equipment without a real development of integrated I/O. Biosensors are suitable devices for transducing biomolecular interactions by converting them into electrical signals. In this work, we explore the latest advancements in biomolecular computing, as well as in biosensors, with focus on technology suitable to provide the required and still missing I/O devices. Therefore, our goal is to picture out the present and future perspectives about DNA, RNA, and enzymatic-based computing according to the progression in its I/O technologies, and to understand how the field of biosensors contributes to the research beyond CMOS

Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices

Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. Results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD. © 2020, The Author(s)

Orientation selectivity in a multi-gated organic electrochemical transistor.

UNLABELLED: Neuromorphic devices offer promising computational paradigms that transcend the limitations of conventional technologies. A prominent example, inspired by the workings of the brain, is spatiotemporal information processing. Here we demonstrate orientation selectivity, a spatiotemporal processing function of the visual cortex, using a poly(3,4ethylenedioxythiophene):poly(styrene sulfonate) ( PEDOT: PSS) organic electrochemical transistor with multiple gates. Spatially distributed inputs on a gate electrode array are found to correlate with the output of the transistor, leading to the ability to discriminate between different stimuli orientations. The demonstration of spatiotemporal processing in an organic electronic device paves the way for neuromorphic devices with new form factors and a facile interface with biology