Abstract: Contributions of organic semiconducting materials to electronics are particularly hard to assess: As macromolecular organizations, they have low enthalpy so they can be processed in soft conditions and they have resilience to deformation. However, for th e same reason, they have also broader density of energy states and more instabilities than silicon in ambient. Controlling matter's order at low scale and its properties for as long as possible were always golden standards for microelectronics. Neverthel ess, in a time where brain functioning rises even more as a source of inspiration, shall it still be so? Here are presented clues on how physical property dispersions may be relevant features for information generator nodes to recognize patterns. In a co ntext where the information to recognize is not trivial to physically define, no model can rule sensors' classification a priori. Despite this, broadening the conducting polymer temporal responses in a sensing array allows recognizing dynamical voltage p atterns, or broadening conducting polymer's chemistry in a sensing array enlarges a classifier's perception field to recognize solvent vapors in air. By the nature of property dispersions in regards to the information to recognize, physical variabilities (structural and chemical) can be assets to exploit for pattern recognition and not necessarily drawbacks to bypass for hardware manufacturing. The brain architecture is also transient: a part of the processed information is engraved in its topology, sho wing that a hardware classifier can make use of physical instabilities as part of its programing, by forming new connections in a nodal architecture. Some evidences are also presented here, on how dendritic morphogenesis of a conducting polymer can be a mean to store past voltage experiences in the impedance between nodes in a topology. Very distinct electrochemical features appear in the readout impedance information after growth and these features are to be associated with the shape of a voltage wave inputted on the junction. By the physical implementation of materials' disorder and transience in electronics devices, it is expected that organic semiconductors will integrate essential ingredients in future-emerging information generator nodes beyond s ensors: from embedded random information generating resources to evolving abilities in information classification architectures.

Abstract: We have adapted a "peel-off" process to structure stacked organic semiconductors (conducting polymers or small molecules) and metal layers for diode microfabrication. The fabricated devices are organic diode rectifier in a coplanar waveguide structure. U nlike conventional lithographic process, this technique does not lead to destroy organic active layers since it does not involve harsh developer or any non-orthogonal solvent that alter the functionality of subsequentially deposited materials. This proce ss also involves recently reported materials, as a p-dopant of an organometallic electron acceptor Copper(II) trifluoromethanesulfonate, that play the role of hole injection layer in order to enhance the performances of the diode. Comparatively to self-a ssembled monolayers based optimized structures, the fabricated diodes show higher reproducibility and stability. High rectification ratio for realized pentacene and poly(3-hexylthiophene) diodes up to 10^6 has been achieved. Their high frequency response has been evaluated by performing theoretical simulations. The results predict operating frequencies of 200 MHz and 50 MHz for pentacene and P3HT diode rectifiers respectively, with an input oscillating voltage of 2 V peak-to-peak, promising for RFID dev ice applications or for GSM band energy harvesting in low-cost IoT objects.

Abstract: Most of today's strategies to interface biology with electronic hardware are based on layered architectures where the front-end of sensing is optimized separately from the back-end for processing/computing signals. Alternatively, biological systems are c apitalizing on distributed architecture where both sensing and computing are mix together and co-optimized. In this talk, we will present our strategy to implement bio-sensing of electroactive cells in a neuromorphic perspective. We will present how orga nic electrochemical transistors can be used to record electrical signals from neural cells. We will show various strategies capitalizing on the versatility of organic materials synthesis and organic device fabrication to tune and adapt the functionalitie s of such bio-sensors. We will then present how these strategies can be efficiently used to realize computing functions directly at the interface with biology. Notably, we will illustrate how a network of ionic sensors can implement the reservoir computi ng concept, a powerful neuromorphic computing approach of particular interest for dynamical signal processing.

Abstract: Electropolymerization is an interesting bottom-up strategy to structure conducting materials at the micro/nano-scale in liquid phase that offers a wide morphological versatility. Since the geometry of these structures governs their electrochemical proper ties, it is fundamental to decipher the mechanisms that rule the polymer assembling upon the electrically-programmed growth to use this phenomenon as a neuro-inspired building block for unconventional information processing. Herein, we investigate variou s electrical parameters of electropolymerization affecting the conducting polymer network geometry. We find that various structures such as dendrites, trees, fractals as well as low-fractality cables can be obtained between the two-wire electrodes, based on applied voltage amplitude, biasing symmetry, bias frequency, the concentration of monomers and electrode configurations. We qualitatively and quantitatively study the relationship between the electrical parameters affecting geometrical parameters of the conducting polymer network as well as electropolymerization dynamics through video and image processing. The systematic analysis shows that an increase in applied voltage leads to higher growth rate, more branches, and lower surface to bulk ratio. At the other hand, an increase in bias frequency leads to higher growth rate, smaller number of branches, and higher surface to volume ratio. In order to model the experimental phenomena, we simulate a simplified version of the problem with two bipolar met al electrodes and an electrolyte filled with dilute moving particles at random positions, reflecting the monomers in the liquid phase. The wire electrodes are biased with AC frequency and the spacio-temporal potential map is evaluated by solving the Lapl ace equation. The motion of the monomer particles is controlled by the electric field and by random Brownian motion. The particles that happen to touch the electrodes are stuck to the electrodes, with a certain probability of sticking. The stuck particle s are incorporated into the electrode, and the potential is recalculated for the motion of the particles;. The simulations are tested for different AC applied voltages, frequency, duty cycles, and voltage offsets. The increase in applied voltage leads to higher sticking probability; resulting in a higher growth rate, multiple branches, and higher density, which goes well with the experiments. Further, the effect of frequency, which is not so intuitive, shows that higher AC frequency, favors linear cable like growth, while lower frequency leads to more isotropic growth, while voltage offset and non-equal duty cycle lead to asymmetrical growth, in accordance with experiments. In addition, the effect of electrode spacing, different electrode designs, elec trical pulse shapes and the concentration of particles, are also studied. The study helps in visualizing the motion of particles in different electrical conditions, which is not possible to probe experimentally. Some subtle experimental features, such as the effect of preferential growth on the tips, and broadening of the electrode before touching, are observed in the modeling studies. Thus, we find that the different network architectures are associated with different Laplace end diffusion fields gover ning the monomers motion and in turn electropolymerized network geometry. Such unconventional engineering route could have a variety of applications from neuromorphic engineering to bottom-up computing strategies.

Abstract: Most of today's strategies to interface biology with electronic hardware are based on layered architectures where the front-end of sensing is optimized separately from the back-end for processing/computing signals. Alternatively, biological systems are c apitalizing on distributed architecture where both sensing and computing are mix together and co-optimized. In this talk, we will present our strategy to implement bio-sensing of electroactive cells in a neuromorphic perspective. We will present how orga nic electrochemical transistors can be used to record electrical signals from neural cells. We will show various strategies capitalizing on the versatility of organic materials synthesis and organic device fabrication to tune and adapt the functionalitie s of such bio-sensors. We will then present how these strategies can be efficiently used to realize computing functions directly at the interface with biology. Notably, we will illustrate how a network of ionic sensors can implement the reservoir computi ng concept, a powerful neuromorphic computing approach of particular interest for dynamical signal processing.

Abstract: One of the neuromorphic engineering aims is using nanoelectronics' materials and devices to reproduce key features that are used by the brain for computing. Currently, neuromorphic engineering has explored standard silicon-based technologies (i.e. such a s complementary metal-oxide-semiconductor ) or more emerging material and devices (iono-electronic materials and resistive memory devices, for example). Most of these technologies are still bounded to a top down approach. However, brain computing largely rely on bottom-up processes. For instance, interconnectivity between cells and formation of communication pathway in neural networks result principaly from bottom-up organization. Here, we show how dendritic growth of organic conductive polymers (PEDOT) can be used to mimic structural branching observed in neural network. Conducting-polymer based dendritic structures with different morphology are synthesized in a two-electrode setup by pulsed voltage-driven electropolymerization derived from state-of-t he-art bipolar AC-electrochemical synthetic methods. We show how various AC signals can lead to a large variety of dendritic structures and PEDOT morphologies. In a second part, such dendritic structures are used to implement functionnal OECTs. More impo rtantly, we focus on the transconductance and memory effects that can be obtained in such dendritic OECTs such as short tem plasticity. We report on the relationship between dendrites morphologies and STP time constant. This work paves the way to new app roaches for neuromorphic engineering, such as structural plasticity and neural network topology exploration.

Abstract: Microelectrode arrays (MEAs) are widely used tools for investigating neural activity. To ensure the best sensitivity of the electronic devices to ionic signals and the lowest information loss, their electrochemical interface must be optimized by lowering their surface impedance, with materials that ensure the highest compatibility with the cells at the same time. Here, we show that by the electropolymerization of thiophene-derivatives, functionalized for higher cell biocompatibility and higher electroch emical performances, one can lower the microelectrodes' surface impedance by the control of the polymer morphology. The microelectrode structuring with bottom-up grown conducting polymers was monitored in-situ by voltage-ramped impedance spectroscopy upo n electropolymerization to track its circuit-elements modification. Iterative impedance modeling over the growth confirmed the material's electrochemical dynamic to be controlled by the gradual modifications of specific discrete circuit elements at diffe rent frequency ranges, thanks to the surface electrodes microstructuring. More particularly, we systematically evidenced a monotonic change of the electrode charging from ideal capacitor to constant phase element dominated modes, due to the bulk charging of the conducting polymer. The evolution of the materials morphology screened by atomic force microscopy and electron microscopy has been confronted to the modification of the materials circuit element, and confirmed distinctive charging modes for the e lectrodes that are governed by their different texturing. In addition to the surface morphology, chemical tuning of the electrodeposited polymer has been performed and showed that a fine tuning of the polymer's glycolation promotes the decrease of the el ectrodes' electrochemical impedance down to -15% compared to the unglycolated polymers thanks to a right balance between ionic permeability and electronic performances. Overall, lower impedance values than commercial MEAs have been systematically reached with performances comparable to spin-coated polymer electrodes', and with low performance dispersion over the whole population of electrodes in the MEAs. With the presented preliminary biocompatibility and stability tests, this study aims is to demonstr ate that unusual microfabrication techniques derived from electrochemistry can provide unique features at the material level to match properties of future emerging bioelectronics technologies to the strong requirements of sensing involving biological mat erials with rich material chemistry and morphology. This work paves the way to new approaches for neuromorphic engineering, such as structural plasticity and neural network topology exploration.

Abstract:

Abstract: Neuromorphic computing proposes to process information inspiring from the brain mechanisms to aim computationally cost-effective and intuitive manners to process complex data. While neural network algorithms have proven their real pote ntial in diverse pattern classification applications, their replication into hardware remains technologically challenging. An interesting solution for such hardware implementation is based on organic electrochemical transistors (O ECTs) that have shown promises in emulating synaptic plasticity at the device level, and coherent communication from one device to another. We demonstrate (i) the bottom-up fabrication of OECT micro-arrays and (ii) the pattern classification task using this technology. We achieved the electro-polymerization of a new p-type accumulation-mode conducting polymer, iteratively on top of 12 micrometric OECT devices in a honeycomb array. We characterized the rich material's morphology of the bottom-up grown organic semiconductor structures and assessed their functionality as synaptic transistor devices. The exploitation of the array for the recognition of pulse-frequency-modulated gate-voltage patterns through an aqueous electrolyte show ed that the pattern recognition can cope with the rich variability of device performances, but also that the recognition benefits from the large dispersion in device property of each individual OECTs. These results announce a new game-c hange for organic and molecular electronics. It shows that the chemical and morphological richness of these electronic materials, for which their disorder induces large property distributions, are actually enabling information processing in a neurom orphic computing context: at the image of our brain which exploits in a powerful way the natural morphological and chemical variability of its dendritic and synaptic network.

Abstract: Biological computing systems are very inspirational objects, from their structure, organization and up to there computing principles for the development of new computing paradigms. Emulating some of these basic concepts in hardware could potentially revo lutionize our way of processing information. This approach needs to consider the neuromorphic computing paradigm in its globality, from the basic sensors level to the data analysis one. In this presentation, we will put the emphasis on organic materials as a promising platform for future neurmorphic engineering solutions. In particular, we will present an innovative approach that relies on both intrinsic micro-sensors' physics and neuromorphic computing concepts to show pattern classification out of a 1 2-unit bio-sensing array. We adapt the proposition of reservoir computing to demonstrate that relevant computing can be realized based on the ionic dynamics in 400-nm channel-length organic electrochemical transistor (OECT) and the key concept of learnin g. Furthermore, we show that this approach can deal efficiently with the high level of variability obtained by bottom-up electro-polymerized OECT. We discuss the effect of the array size and variability on the performances for a simple real-time classifi cation task paving the way to new sensing and processing approaches.

Abstract: Organic diode rectifier have attracted a lot of attention recently for RF energy harvesting, and much effort has been applied toward extending the ultra-high frequency range. An important parameter that should be considered for diodes used in RF rectifie r is the turn on voltage, which should be low to overcome the problem of the low voltage generated and power extracted from energy harvesting. In this work, we focused on pentacene organic rectifier with high rectification ratio and low threshold voltage obtained by tuning the work function of gold with a self-assembled monolayer of PFBT and optimizing the thickness of the organic layer. We demonstrate a high rectification ratio up to 107 and a very low turn on voltage as low as 20 mV. Flexible rectifie r diode have been also fabricated in release paper WO84, high rectification ratio of 106 was obtained even after bending of the device. The pentacene based rectifier diodes were also demonstrated to operate at more than 1GHz. This provides a great potent ial for fabricating high-performance organic flexible diodes and opens the way for the development of high frequency response using organic materials.

Abstract: In this work, we propose an electrical strategy to extract further more information on an electrolyte cation's nature than its concentration, out of a single organic electrochemical transistor (OECT): a biocompatible device which is nowadays attracting v ery much attention as a sensor platform for bioelectronics. Based on an optimized OECT structure,the systematic study by impedance spectroscopy of 6 different chloride salts over 12 different concentrations demonstrated that the impedance of the OECT dev ice is governed either by electrical or electrolytic transport mechanisms, depending on the frequency range of the study. From both of these ion-dependent impedances, on can extract either the conductance of the electrolytes or the one of the dedoped pol y(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS). From the boundaries of both of these very different ion-limited mechanisms, we extracted two uncorrelated ion-dependent output, accessing to information on the electrolyte cation's nature apart from its concentration. This strategy can be implemented in dynamic analysis of complex ionic media, such as for cellular-activity sensing, in order to discriminate cationic flows, without introducing any foreign ion-selective material, which can t hreaten the biocompatibility.

Abstract: We report on hydrazine-sensing organic electrochemical transistors (OECTs) with a design consisting in concentric annular electrodes. The design engineering of these OECTs was motivated by the great potential of using OECT sensing arrays in fields such a s bioelectronics. In this work, PEDOT:PSS-based OECTs have been studied as aqueous sensors, specifically sensitive to the lethal hydrazine molecule. These amperometric sensors have many relevant features for the development of hydrazine sensors, such as a sensitivity down to 10^-5 M of hydrazine in water, an order of magnitude higher selectivity for hydrazine than for 9 other water soluble common analytes, the capability to recover entirely its base signal after water flushing and a very low v oltage operation. The specificity for hydrazine to be sensed by our OECTs is caused by its catalytic oxidation at the gate electrode and enables increasing the output current modulation of the devices. This has permitted the device-geometry study of the whole series of 80 micrometric OECT devices with sub-20-nm PEDOT:PSS layers, channel lengths down to 1 μm and a specific device geometry of coplanar and concentric electrodes. The numerous geometries unravel new aspects of the OECT mechanisms governi ng the electrochemical sensing behaviours of the device, more particularly the effect of the contacts which are inherent at the micro-scale. By lowering the device cross-talking, micrometric gate-integrated radial OECTs shall contribute to the diminishin g of the readout invasiveness and therefore promotes further the development of OECT biosensors.