How can the brain be such a good computer? Part of the answer lies in the astonishing number of neurons and synapses that process electrical impulses in parallel. Part of it must be found in the ability of the nervous system to evolve in response to external stimuli and grow, sharpen, and depress synaptic connections. However, we are far from understanding even the basic mechanisms that allow us to think, be aware, recognize patterns, and imagine. The brain can do all this while consuming only around 20 Watts, out-competing any human-made processor in terms of energy-efficiency. This question is of particular interest in a historical era and technological stage where phrases like machine learning and artificial intelligence are more and more widespread, thanks to recent advances produced in the field of computer science. However, brain-inspired computation is today still relying on algorithms that run on traditional silicon-made, digital processors. Instead, the making of brain-like hardware, where the substrate itself can be used for computation and it can dynamically update its electrical pathways, is still challenging. In this work, I tried to employ organic semiconductors that work in electrolytic solutions, called organic mixed ionic-electronic conductors (OMIECs) to build hardware capable of computation. Moreover, by exploiting an electropolymerization technique, I could form conducting connections in response to electrical spikes, in analogy to how synapses evolve when the neuron fires. After demonstrating artificial synapses as a potential building block for neuromorphic chips, I shifted my attention to the implementation of such synapses in fully operational networks. In doing so, I borrowed the mathematical framework of a machine learning approach known as reservoir computing, which allows computation with random (neural) networks. I capitalized my work on demonstrating the possibility of using such networks in-vivo for the recognition and classification of dangerous and healthy heartbeats. This is the first demonstration of machine learning carried out in a biological environment with a biocompatible substrate. The implications of this technology are straightforward: a constant monitoring of biological signals and fluids accompanied by an active recognition of the presence of malign patterns may lead to a timely, targeted and early diagnosis of potentially mortal conditions. Finally, in the attempt to simulate the random neural networks, I faced difficulties in the modeling of the devices with the state-of-the-art approach. Therefore, I tried to explore a new way to describe OMIECs and OMIECs-based devices, starting from thermodynamic axioms. The results of this model shine a light on the mechanism behind the operation of the organic electrochemical transistors, revealing the importance of the entropy of mixing and suggesting new pathways for device optimization for targeted applications.

The development of synthetic life once envisioned by Feynman and Flynn many decades ago has stimulated significant research in materials science, biology, neuroscience, robotics, and computer science. The cross-disciplinary effort and advanced technologies in soft miniature robotics have addressed some of the significant challenges of actuation, sensing, and subsystem integration. An ideal Soft motile miniaturised robot (SMMRs) has innovative applications on a small scale, for instance, drug delivery to environmental remediation. Such a system demands smart integration of micro/nano components such as engines, actuators, sensors, controllers, and power supplies, making it possible to implement complex missions controlled wirelessly. Such an autonomous SMMR spans over multiple science and technology disciplines and requires innovative microsystem design and materials. Over the past decade, tremendous efforts have been made towards mastering one of such a SMMR's essential components: micro-engine. Chemical fuels and magnetic fields have been employed to power the micro-engines. However, it was realized seven years ago in work of TU-Chemnitz Professorship of Material Systems in Nanoelectronics and institute of investigative Nanosciences Leibniz IFW Dresden including Chemnitz side. Write explicitly that it is essential to combine the micro-engine with other functional microelectronic components to create an individually addressable smart and motile microsystem. This PhD work summarises the progress in designing and developing a novel flexible and motile soft micro autonomous robotic tubular systems (SMARTS) different from the well-studied single-tube catalytic micro-engines and other reported micromotors. Our systems incorporate polymeric nanomembranes fabricated by photolithography and rolled-up nanotechnology, which provide twin-tube structures and a spacious platform between the engines used to integrate onboard electronics. Energy can be wirelessly transferred to the catalytic tubular engine, allowing control over the SMARTS direction. Furthermore, to have more functionality onboard, a micro-robotic arm was integrated with remote triggering ability by inductive heating. To make the entire system smart, it is necessary to develop an onboard processor. However, the use of conventional Si technology is technically challenging due to the high thermal processes. We developed complex integrated circuits (IC) using novel single crystal-like organic and ZnO-based transistors to overcome this issue. Furthermore, a novel fabrication methodology that combines with six primary components of an autonomous system, namely motion, structure, onboard energy, processor, actuators, and sensors to developing novel SMARTSs, is being pursued and discussed.:List of acronyms 8 Chapter 1. Introduction 12 1.1 Motivation 14 1.2 Objectives 17 1.3 Thesis structure 18 Chapter 2. Building blocks of micro synthetic life 19 2.1 Soft structure 20 2.1.1 Polymorphic adaptability 20 2.1.2 Dynamic reconfigurability 20 2.1.3 Continuous motion 21 2.2 Locomotion 21 2.2.1 Aquatic 22 2.2.2 State-of-the-art aquatic SMMR 24 2.2.3 State-of-the-art terrestrial SMMR 25 2.2.4 State-of-the-art aerial SMMR 27 2.3 Onboard sensing 28 2.3.1 State-of-the-art 3D and flexible sensors systems 28 2.4 Onboard actuation 30 2.4.1 State-of-the-art actuators 30 2.5 Embedded onboard intelligence 32 2.5.1 State-of-the-art flexible integrated circuits 32 2.6 Onboard energy 33 2.6.1 State-of-the-art micro energy storage 34 2.6.2 State-of-the-art onboard energy harvesting SMMR 35 Chapter 3. Technology overview 38 3.1 Structure 38 3.1.1 Self-assembled “swiss-roll” architectures 40 3.1.2 Polymeric “swiss-roll” architectures 41 3.2 Motion: micro tubes as propulsion engines 44 3.2.1 Chemical engines 44 3.3 Embedded onboard intelligence 46 3.3.1 Thin film transistor 46 3.3.2 Basic characteristics of MOSFETs 48 3.4 Growth dynamics of organic single crystal films 51 3.4.1 Thin films growth dynamics 52 3.5 Powering SMARTSs 55 3.5.1 Onboard energy storage 56 3.5.2 Wireless power delivery 59 3.6 Integrable micro-arm 63 3.6.1 Stimuli-responsive actuator 63 3.6.2 Remote activation 64 Chapter 4. Fabrication and characterization 65 4.1 Thin film fabrication technology 65 4.1.1 Photolithography 65 4.1.2 E-beam deposition 68 4.1.3 Sputtering 69 4.1.4 Physical vapour deposition 70 4.1.5 Atomic layer deposition 71 4.1.6 Ion beam etching 72 4.2 Characterization methods 73 4.2.1 Atomic force microscopy 73 4.2.2 Scanning electron microscopy 74 4.2.3 Cyclic voltammetry 75 4.2.4 Galvanic charge discharge 77 4.2.5 Electrochemical impedance spectroscopy 78 Chapter 5. Development of soft micro autonomous robotic tubular systems (SMARTS) 81 5.1 Soft, flexible and robust polymeric platform 82 5.2 Locomotion of SMARTS 84 5.2.1 Assembly of polymeric tubular jet engines 84 5.2.2 Catalytic self-propulsion of soft motile microsystem 85 5.2.3 Propulsion power generated by the catalyst reaction 87 5.3 Onboard energy for SMARTS 89 5.3.1 Onboard wireless energy 90 5.3.2 Onboard ‘zero-pitch’ micro receiver coil 90 5.3.3 Evaluation of the micro receiver coil 91 5.4 Onboard energy storage 92 5.4.1 Fabrication of nano-biosupercapacitors 93 5.4.2 Electrochemical performance of “Swiss-roll” nBSC 97 5.4.3 Self-discharge performance and Bio enhancement: 98 5.4.4 Electrochemical and structural life time performance 100 5.4.5 Performance under physiologically conditions 101 5.4.6 Electrolyte temperature and flow dependent performance 102 5.4.7 Performance under hemodynamic conditions 105 5.4.8 Biocompatibility of nBSCs 105 5.5 Wireless powering and autarkic operation of SMARTS 108 5.5.1 Remote activation of an onboard IR-LED 108 5.5.2 Wireless locomotion of SMARTS 109 5.5.3 Effect of magnetic moment on SMARTS locomotion 111 5.5.4 Full 2D wireless locomotion control of SMARTS 112 5.5.5 Self-powered monolithic pH sensor system 114 5.6 Onboard remote actuation 119 5.6.1 Fabrication of integrable micro-arm 120 5.6.2 Remote actuation of integrable micro-arm 122 5.7 Flexibility of SMARTS 122 5.8 Onboard integrated electronics 123 5.9 Onboard organic electronics 124 5.9.1 Growth of BTBT-T6 as active semiconductor material 125 5.9.2 Confined Growth of BTBT-T6 to form Single-Crystal-Like Domain 128 5.9.3 Fabrication of OFET based on Single-Crystal-Like BTBT-T6 129 5.9.4 Carrier injection optimization 132 5.9.5 Performance of single-crystal-like BTBT-T6-OFET 133 5.10 Onboard flexible metal oxide electronics 136 5.10.1 Fabrication flexible ZnO TFT 138 5.10.2 Performance of ZnO TFT 139 5.10.3 Flexible integrated circuits 140 5.10.4 Logic gates 140 Chapter 6. Summary 142 Chapter 7. Conclusion and outlook 144 References 147 List of Figures & tables 173 Versicherung 177 Acknowledgement 178 Research achievements 180 Research highlight 183 Cover pages 184 Theses 188 Curriculum-vitae 191

The rapid developments in the field of DNA computing reflects two substantial questions: 1. Which models for DNA based computation are really universal? 2. Which model fulfills the requirements to a universal lab-practicable programmable DNA computer that is based on one of these models? This paper introduces the functional model DNA-HASKELL focussing its lab-practicability. This aim could be reached by specifying the DNA based operations in accordiance to an analysis of molecular biological processes. The specification is determined by an abstraction level that includes nucleotides and strand end labels like 5'-phosphate. Our model is able to describe DNA algorithms for any NP-complete problem - here exemplified by the knapsacik problem - as well as it is able to simulate some established mathematical models for computation. We point out the splicing operation as an example. The computational completeness of DNA-HASKELL can be supposed. This paper is based on discussions about the potenzial and limits of DNA computing, in particular the practicability of a universal DNA computer.

The rapid developments in the field of DNA computing reflects two substantial questions: 1. Which models for DNA based computation are really universal? 2. Which model fulfills the requirements to a universal lab-practicable programmable DNA computer that is based on one of these models? This paper introduces the functional model DNA-HASKELL focussing its lab-practicability. This aim could be reached by specifying the DNA based operations in accordiance to an analysis of molecular biological processes. The specification is determined by an abstraction level that includes nucleotides and strand end labels like 5'-phosphate. Our model is able to describe DNA algorithms for any NP-complete problem - here exemplified by the knapsacik problem - as well as it is able to simulate some established mathematical models for computation. We point out the splicing operation as an example. The computational completeness of DNA-HASKELL can be supposed. This paper is based on discussions about the potenzial and limits of DNA computing, in particular the practicability of a universal DNA computer.

In the realm of neuroelectronics, the challenge lies in achieving finer observations of physiological processes to comprehend neuronal interactions and computations. This necessitates the development of more compliant and biomimetic interfaces for improved integration with biological tissues, enabling finer physiological process observations. Commonly used flat and static electrode interfaces contrast sharply with the dynamic, complex, and three dimensional (3D) extracellular matrix (ECM) in which cells reside. Introducing 3D patterns on electrode surfaces enhances cell-chip coupling, improving the signal recording. Moreover, inorganic electrodes are stiff and rigid, creating mechanical mismatches with softer biological tissues, and they fail to fully capture ionic conduction.This thesis addresses these challenges by focusing on designing and engineering a multi-layer dynamic and stimuli-responsive bioelectronic interface. The system combines light-responsive, deformable polymers like Poly(Disperse Red 1-methacrylate) (pDR1m) with conductive polymers such as Poly(3,4-ethylenedioxythiophene): poly(stirensulfonate) (PEDOT:PSS). pDR1m responds to light, exhibiting 3D surface topography deformation, while PEDOT:PSS facilitates electrical recording and stimulation of cells, offering mixed electronic and ionic conduction as well as good mechanical properties. The potential use of an intermediate Polydimethylsiloxane (PDMS) film to improve layer adhesion is also explored. The individual and multi-layer samples were first optimized for spin coating manufacturing, and then thoroughly characterized to investigate their thickness, morphology, optical and electrochemical properties. Patterning of pDR1m-based samples was carried out using laser scanning confocal microscopy and a Lloyd’s mirror interferometer.The pDR1m\PEDOT:PSS sample demonstrates promising morphological and conductive properties, and the presence of PEDOT:PSS does not alter the absorption spectra of pDR1m. The multi-layer approach also supports efficient inscription of 3D surface reliefs without damaging the conductive layer. In conclusion, this work successfully designs conductive and dynamic light-driven films, which showcase good potential for bioelectronics and neuroelectronic interfaces. These interfaces could lead to enhanced investigations into combined electromechanical stimulation on cells and provide a more biomimetic coupling with biological tissues. / Inom neuroelektronikens område ligger utmaningen i att uppnå finare observationer av fysiologiska processer för att förstå neuronala interaktioner och beräkningar. Detta kräver utveckling av mer följsamma och biomimetiska gränssnitt för förbättrad integration med biologiska vävnader, vilket möjliggör finare fysiologiska processobservationer. Vanligt använda platta och statiska elektrodgränssnitt står i skarp kontrast till den dynamiska, komplexa och tredimensionella (3D) extracellulära matrisen (ECM) i vilken celler finns. Att introducera 3D-mönster på elektrodytor förbättrar cell-chip-kopplingen, vilket förbättrar signalinspelningen. Dessutom är oorganiska elektroder styva och stela, vilket skapar mekaniska felmatchningar med mjukare biologiska vävnader, och de lyckas inte helt fånga jonledning.Den här avhandlingen tar upp dessa utmaningar genom att fokusera på att designa och konstruera ett flerlagers dynamiskt och stimuli-responsivt bioelektroniskt gränssnitt. Systemet kombinerar ljuskänsliga, deformerbara polymerer som Poly(Disperse Red 1-methacrylate) (pDR1m) med ledande polymerer som Poly(3,4-etylendioxitiofen): poly(stirensulfonat) (PEDOT:PSS). pDR1m reagerar på ljus och uppvisar 3D-yttopografideformation, medan PEDOT:PSS underlättar elektrisk inspelning och stimulering av celler, erbjuder blandad elektronisk och jonledning samt goda mekaniska egenskaper. Den potentiella användningen av en mellanliggande polydimetylsiloxan (PDMS) film för att förbättra skiktvidhäftningen undersöks också. De individuella och flerskiktiga proverna optimerades först för spinnbeläggningstillverkning och karakteriserades sedan grundligt för att undersöka deras tjocklek, morfologi, optiska och elektrokemiska egenskaper. Mönster av pDR1m-baserade prover utfördes med laserskanning konfokalmikroskopi och en Lloyds spegelinterferometer.pDR1m\PEDOT:PSS-provet visar lovande morfologiska och ledande egenskaper, och närvaron av PEDOT:PSS förändrar inte absorptionsspektra för pDR1m. Flerskiktsmetoden stöder också effektiv inskription av 3D-ytreliefer utan att skada det ledande lagret. Sammanfattningsvis designar detta arbete framgångsrikt ledande och dynamiska ljusdrivna filmer, som visar upp god potential för bioelektronik och neuroelektroniska gränssnitt. Dessa gränssnitt kan leda till förbättrade undersökningar av kombinerad elektromekanisk stimulering på celler och ge en mer biomimetisk koppling med biologiska vävnader.