Ultra conformable and multimodal tactile sensors based on organic field-effect transistors

Cognitive psychology is the branch of psychology related to all the processes by which sensory input is transformed, processed and used. Academic and industrial research has always invested time and resources to develop devices capable to simulate the behavior of the organs where the perceptions are located. In recent years, in fact, there have been numerous discoveries related to new materials, and new devices, capable of reproducing, in a reliable manner, the sensory behavior of humans. Particular interest in scientific research has been aimed at understanding and reproducing of man's tactile sensations. It is known that, through the receptors of the skin, it is possible to detect sensations such as pain, changes in pressure and/or temperature. The development of tactile sensor technology had a significant increase in the last years of 1970s, thanks to the important surveys of Stojiljkovic, Harmon and Lumelsky who presented the firsts prototype of sensors for artificial skin applications, and summarized the main characteristics and requirements of tactile sensors. Recently, organic electronics has been deeply investigated as technology for the fabrication of tactile sensors using biocompatible materials, which can be deposited and processed on ultra flexible and ultra conformable substrates. In general, the most attractive property of these materials is mainly related to their high mechanical flexibility, which is mandatory for artificial skin applications. The main object of this PhD research activity was the development and optimization of an innovative technology for the realization of physical sensors able to detect pressure and temperature variations, which can be applied in the field of biomedical engineering and biorobotics. By exploiting the particular characteristics of the employed materials, such as mechanical flexibility, the proposed sensors are very suitable to be integrated with flexible structures (for example plastics) as a pressure and temperature sensor, and therefore, ideal for the realization of an artificial skin like. In Chapter 1, the basics of humans somatosensory system will be introduced: after a brief description of tactile thermoreceptors, mechanoreceptors and nociceptors, a definition of electronic skin and its characteristics will be provided. In Chapter 2, a wide analysis of the state of the art will be reported. Several and different examples of tactile sensor (in inorganic and organic technology) will be presented, underlining advantages and disadvantages for each approach. In Chapter 3, the firsts experimental results, obtained in the first part of my PhD program, will be presented. All the steps of the fabrication process of the devices will be described, as well as the measurement setup used for the electrical characterization of the sensors. In Chapter 4, the sensor structure optimization will be presented. It will be demonstrated how the presented devices are able to sense simultaneously thermal and mechanical stimuli. Moreover, it will be demonstrated that, thanks to an alternative and innovative fabrication process, the sensors can be transferred directly on skin, thus proving the suitability of the proposed sensor architecture for tactile applications

Computer Vision Aided Print Pattern Generation in Inkjet Printed Electronics

Inkjet printed electronics is one of the new promising electronics manufacturing techniques out there. It has become a widely adopted manufacturing method especially in the field of low-cost electronics. This thesis considers an application of inkjet printed electronics where conductive ink is used for printing the connections between the components on a single unit called a module. The base module is fabricated by molding the components together such that the connection points of the components form a level surface. After this, the wiring is printed on top. Because of the inaccuracies in the fabrication process, there is often a mismatch between the designed print pattern and the target module. The purpose of this thesis is to introduce an online print pattern generation system that uses computer vision to detect the locations of the module components and then modifies the print pattern accordingly. By integrating the print pattern generation system as a part of the manufacturing process, not only is it possible to print functioning modules but also multiple modules can be printed at the same time. This way the capabilities of inkjet printed electronics can be more efficiently harnessed. The experiments prove that the developed print pattern correction system together with the proposed imaging setup are able to produce desired results in practice. In addition to successfully printing ten modules at once, it is also shown that the developed system is robust and generalizes well for different types of modules. /Kir1

Predictive modeling using sparse logistic regression with applications

In this thesis, sparse logistic regression models are applied in a set of real world machine learning applications. The studied cases include supervised image segmentation, cancer diagnosis, and MEG data classification. Image segmentation is applied both in component detection in inkjet printed electronics manufacturing and in cell detection from microscope images. The results indicate that a simple linear classification method such as logistic regression often outperforms more sophisticated methods. Further, it is shown that the interpretability of the linear model offers great advantage in many applications. Model validation and automatic feature selection by means of L1 regularized parameter estimation have a significant role in this thesis. It is shown that a combination of a careful model assessment scheme and automatic feature selection by means of logistic regression model and coefficient regularization create a powerful, yet simple and practical, tool chain for applications of supervised learning and classification

Learning-Based Data-Driven and Vision Methodology for Optimized Printed Electronics

Inkjet printing is an active domain of additive manufacturing and printed electronics due to its promising features, starting from low-cost, scalability, non-contact printing, and microscale on-demand pattern customization. Up until now, mainstream research has been making headway in the development of ink material and printing process optimization through traditional methods, with almost no work concentrated on machine learning and vision-based drop behavior prediction, pattern generation, and enhancement. In this work, we first carry out a systematic piezoelectric drop on demand inkjet drop generation and characterization study to structure our dataset, which is later used to develop a drop formulation prediction module for diverse materials. Machine learning enables us to predict the drop speed and radius for particular material and printer electrical signal configuration. We verify our prediction results with untested graphene oxide ink. Thereafter, we study automated pattern generation and evaluation algorithms for inkjet printing via computer vision schema for several shapes, scales and finalize the best sequencing method in terms of comparative pattern quality, along with the underlying causes. In a nutshell, we develop and validate an automated vision methodology to optimize any given two-dimensional patterns. We show that traditional raster printing is inferior to other promising methods such as contour printing, segmented matrix printing, depending on the shape and dimension of the designed pattern. Our proposed vision-based printing algorithm eliminates manual printing configuration workload and is intelligent enough to decide on which segment of the pattern should be printed in which order and sequence. Besides, process defect monitoring and tracking has shown promising results equivalent to manual short circuit, open circuit, and sheet resistance testing for deciding over pattern acceptance or rejection with reduced device testing time. Drop behavior forecast, automatic pattern optimization, and defect quantization compared with the designed image allow dynamic adaptation of any materials properties with regards to any substrate and sophisticated design as established here with varying material properties; complex design features such as corners, edges, and miniature scale can be achieved

Learning-Based Data-Driven and Vision Methodology for Optimized Printed Electronics

Inkjet printing is an active domain of additive manufacturing and printed electronics due to its promising features, starting from low-cost, scalability, non-contact printing, and microscale on-demand pattern customization. Up until now, mainstream research has been making headway in the development of ink material and printing process optimization through traditional methods, with almost no work concentrated on machine learning and vision-based drop behavior prediction, pattern generation, and enhancement. In this work, we first carry out a systematic piezoelectric drop on demand inkjet drop generation and characterization study to structure our dataset, which is later used to develop a drop formulation prediction module for diverse materials. Machine learning enables us to predict the drop speed and radius for particular material and printer electrical signal configuration. We verify our prediction results with untested graphene oxide ink. Thereafter, we study automated pattern generation and evaluation algorithms for inkjet printing via computer vision schema for several shapes, scales and finalize the best sequencing method in terms of comparative pattern quality, along with the underlying causes. In a nutshell, we develop and validate an automated vision methodology to optimize any given two-dimensional patterns. We show that traditional raster printing is inferior to other promising methods such as contour printing, segmented matrix printing, depending on the shape and dimension of the designed pattern. Our proposed vision-based printing algorithm eliminates manual printing configuration workload and is intelligent enough to decide on which segment of the pattern should be printed in which order and sequence. Besides, process defect monitoring and tracking has shown promising results equivalent to manual short circuit, open circuit, and sheet resistance testing for deciding over pattern acceptance or rejection with reduced device testing time. Drop behavior forecast, automatic pattern optimization, and defect quantization compared with the designed image allow dynamic adaptation of any materials properties with regards to any substrate and sophisticated design as established here with varying material properties; complex design features such as corners, edges, and miniature scale can be achieved

Rapid prototyping of 3D Organic Electrochemical Transistors by composite photocurable resin

Rapid Prototyping (RP) promises to induce a revolutionary impact on how the objects can be produced and used in industrial manufacturing as well as in everyday life. Over the time a standard technique as the 3D Stereolithography (SL) has become a fundamental technology for RP and Additive Manufacturing (AM), since it enables the fabrication of the 3D objects from a cost-efective photocurable resin. Eforts to obtain devices more complex than just a mere aesthetic simulacre, have been spent with uncertain results. The multidisciplinary nature of such manufacturing technique furtherly hinders the route to the fabrication of complex devices. A good knowledge of the bases of material science and engineering is required to deal with SL technological, characterization and testing aspects. In this framework, our study aims to reveal a new approach to obtain RP of complex devices, namely Organic Electro-Chemical Transistors (OECTs), by SL technique exploiting a resin composite based on the conductive poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) and the photo curable Poly(ethylene glycol) diacrylate (PEGDA). A comprehensive study is presented, starting from the optimization of composite resin and characterization of its electrochemical properties, up to the 3D OECTs printing and testing. Relevant performances in biosensing for dopamine (DA) detection using the 3D OECTs are reported and discussed too

Integrated Pressure/Temperature Sensor Array Based on Nickel Conductive Composite

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 홍용택.Implementation of electronic artificial skin has been widely studied, from basic concept to prototypes, for potential applications in robot engineering and prosthetic replacement. Electronic Artificial skin plays a key role of sensing external environment, such as pressure and temperature, and delivering transformed signals either to robot control or human nerve system. In order to truly mimicking human skin, artificial skin at least needs to contain both pressure and temperature sensing elements in an array format. In fact, a couple of trials have been attempted to integrate sensing both elements onto single skin. Combination of commercial temperature sensing chips with printed pressure sensitive resistor or assembly of separately fabricated sensor arrays of each type has been demonstrated. These hybrid type integration or assembly approach renders rather complicated processes and thus increases fabrication cost. For sensing elements, conductive composite materials have been commonly used, whose resistance changes as geometrical dimension changes with applied pressure or temperature. In most cases, the conductive composite materials have been used only for single type of sensing element, either pressure or temperature sensor. It is challenging to differentiate two type of sensing part in one substrate with single conductive composite material and to independently read out each signal. Therefore, there have been no reported researches on using single conducting composite materials to a multi-sensing device. In addition, the conductive composite materials were typically fabricated "on" either flexible or stretchable substrate only after readout active-matrix circuitry was fabricated on the substrate. Therefore, there can be limitation in selection of materials and device structure, and process incompatibility that can makes mass manufacturing of the active-matrix sensor arrays difficult. However, when the sensor arrays are separately fabricated by embedding the sensing elements in the substrate, they can be easily incorporated into passive-matrix system or can be simply laminated on the separately fabricated active-matrix circuitry, as in case of the electronic paper front-plane technology. In this thesis, a simple fabrication method of integrated pressure/temperature sensor arrays by embedding conductive nickel (Ni) particles in poly(dimethyloxane) (PDMS) medium for electronic artificial skin application will be elucidated. The pressure and temperature sensing parts are formed in one pixel but have different heights, which are implemented by introducing a corrugated structure to Ni/PDMS composite with a pre-patterned aluminum mold. Since Ni particles are ferromagnetic materials, Ni/PDMS mixture can be patterned by exposure to patterned magnetic fields. Magnetic field exposure helps both lateral patterning and vertical particle alignment, which directly improved sensitivity and linearity of the sensor. Independent and stable read-out signals for pressure and temperature sensors are successfully obtained even under repeated measurements. This technology has advantages of simple tuning for sensitivity and operation ranges by changing particle concentration and device physical dimension, easy scaling-up to large area by seamlessly bonding small arrays or using large-area magnetic field modulator, and potential implementation of the sensor frontplane for active-matrix backplane read-out circuitry. Electronic artificial skin passive-matrix system with about 10 ppi resolution with the integrated 16 by 16 pressure and 15 by 15 temperature sensor arrays have been finally demonstrated. Furthermore, a highly stretchable electrode with demonstration of a resolution sustaining lighting device by fully utilizing the magnetic patterning/aligning method will be also studied. This stretchable electrode based on conductive composite shows unique property that is negative strain-dependency in electrical resistance. Although cyclic behavior of pure nickel composite needs more improvement, nickel-based composite materials have excellent advantages over other materials in terms of simple patterning and in-situ embedding in the matrix. This novel technology would be one of the key enabling technology in implementing future stretchable electronic display devices.Abstract i Contents ６ List of Figures ９ List of Tables １３ Chapter 1 Introduction １４ 1.1 Motivation １４ 1.2 Human Sense of Touch ２１ 1.2.1 Tactile Receptors ２４ 1.2.2 Thermoeceptor ２６ 1.2.3 Nociceptors ２６ 1.2.4 Kinesthetic Receptors ２７ 1.2.5 Tactile Sensitivity and Acuity ２７ 1.2.6 Stretchability of Human Body ２８ 1.3 Transduction Principles for Electronic Skin Applications ３０ 1.3.1 Piezoresistive ３０ 1.3.2 Piezoelectric ３４ 1.3.3 Capacitive ３５ 1.3.4 Optical ３７ 1.4 The Goal and Outline of This Thesis ４０ Chapter 2 Nickel Conductive Composite Material : Characteristics Enhancement by Magnetic Aligning Method ５６ 2.1 Introduction ５６ 2.2 Theoretical Analysis with the Maxwell Theory and the Effective Medium Theory ６２ 2.3 Materials and Fabrication Method ６７ 2.4 Results and Discussions ６９ 2.4.1 Optical Microscope Measurement ６９ 2.4.2 Electrical Characteristics ７０ 2.5 Conclusion ７５ Chapter 3 Scalable and Stretchable Fully Integrated Pressure/Temperature Sensor Array with Magnetically Aligned and Patterned Nickel Conductive Composite Material ８７ 3.1 Introduction ８７ 3.2 Materials and Fabrication Method ９１ 3.3 Finite Element Analysis for Patterning and Mechanical Characteristics ９５ 3.4 Electrical Characteristics of Integrated Sensor Array １１７ 3.5 Conclusion １２０ Chapter 4 Negatively Strain-Dependent Electrical Resistance of Magnetically Arranged Nickel Composite : Its Application to Highly Stretchable Electrode and Stretchable Lighting Devices １２５ 4.1 Introduction １２５ 4.2 Experimental １３０ 4.3 Results and Analysis １３５ 4.3.1 Electrical Characteristics with Tension Test １３５ 4.3.2 Analysis with Three-dimensional Percolation Theory １３８ 4.3.3 Highly Stretchable Electrode with Ink-jet Printed Silver １４５ 4.4 Resolution Sustainable Stretchable Lighting Device １４９ 4.5 Conclusion １５３ Chapter 5 Conclusion １６２ Abstract in Korean １７０Docto

Printable organic and inorganic materials for flexible electrochemical devices

Portuguese Science Foundation - project Electra PTDC/CTM/099124/2008 and the PhD grant SFRH/BD/45224. financial support: Professor E. Fortunato’s ERC 2008 Advanced Grant (INVISIBLE contract number 228144), “APPLE” FP7-NMP-2010-SME/262782-2 and “SMARTEC” FP7-ICT-2009.3.9/25820

Hybrid Nanomaterials

Two of the hottest research topics today are hybrid nanomaterials and flexible electronics. As such, this book covers both topics with chapters written by experts from across the globe. Chapters address hybrid nanomaterials, electronic transport in black phosphorus, three-dimensional nanocarbon hybrids, hybrid ion exchangers, pressure-sensitive adhesives for flexible electronics, simulation and modeling of transistors, smart manufacturing technologies, and inorganic semiconductors