Abstract: Advances on System-On-Chip and organic sensors allows the development of miniaturized impedance measurement hardware for gas monitoring in IoT. In this work, we present the development of miniaturized, multiplexed, and connected platform for impedance sp ectroscopy. Designed for online measurements and adapted to wireless network architectures, our platform has been tested and optimized to be used for multi-selective chemical organic sensor nodes. Our designed circuit is built from low cost and low power consumption microelectronics components providing real time acquisition. The proposed system is based on ESP32 Microcontroller enabling the management of an impedance network analyzer AD5933 (Analog Devices, Norwood, MA, USA) through its I2C interface. Our system benefits from two multiplexer components allowing calibration process and the interface of 15 conductimetric sensors with fast acquisition (less than 90 ms per acquisition). The paper describes the microelectronics design, the impedance respon se over time, the measurement's sensitivity and accuracy and the testing of the platform with embedded chemical sensors for gas classification and recognition.

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: Unlike living organisms, electronics is not metamorphic: devices are mass-produced in series, normalized with well-defined standards, aim at being highly stable during operation and are rebutted with poor recyclability when a better version of themselves can be deployed. Software can also be upgraded on the same hardware with no waste, but not the hardware. In nature, systems grow, evolve, copy, adapt to the environment they sense, scavenge only the least natural resources they need to operate and degra de to biomass. Very often, their degree of intelligence is associated to their plasticity and ability to adapt to an environment, proliferating on a substrate in the harshest conditions.-Today, electrochemistry can be a building block for a field of elec tronics which aims at mimicking natural intelligence by growing electro-conductive topologies as a learning mechanism. Specifically, electropolymerization allows on demand neurogenesis of sensitive inputs with chemospecific semiconductors, and morphogene sis of conducting polymer dendritic topologies. As bottom-up strategy, it is a mask-less patterning technique for microelectrodes and devices with high control of thickness/roughness. Like additive-manufacturing, it consumes only what it needs of the mat erial resources available in an electroactive electrolyte environment, to manufacture semiconductors at various doping levels and bandgaps with no precious mineral and ore, and with low energy consumption manufactured in ambient. Fab-less, dynamic electr opolymerization can be used as in operando mechanism to program the generation of sensing elements, the routing of array of devices, in a way brain cells, micellia or roots grow topologies on specific landscapes to achieve their purpose. In an era where new computing paradigms are needed, with manufacturing methods adopting the most care for the environment, electropolymerization inspires from nature to propose new ways hardware can form and embed new learning functionalities by physically evolving.

Abstract: In the quest to change the way we envision computing, the ability to create connections in between computing nodes on demand offers the very exciting possibility to explore bottom-up strategies when it comes to thinking the design of electronic devices a nd circuits. Recently, the growth of conductive polymer fibers via electropolymerization (most notably poly(3,4-ethylenedioxythiophene) doped with poly(styrenesulfonate), abbreviated as PEDOT:PSS) was employed in the realm of neuromorphic hardware, first as a way to tune the resistance of a connection, and then to perform computing tasks, such as biosignal classification trough reservoir computing, thus establishing the advantages of the method.In this work, we propose to demonstrate that electropolymer ization is a useful tool for the creation of sensors that are able to realize computing tasks in an aqueous environment. By taking advantage of the morphology of the fibers, we show that PEDOT:PSS dendrites can discriminate between different types of vol tage pulses emitted in their vicinity by a local gate electrode, thus performing in materio classification.In addition, we discuss the growth of networks of polymer fibers on 2D substrates. The ability to create structures that cover several microelectro des allows us to study the behavior of the whole system instead of a single dendrite, and it highlights the relationship that relates the morphology of the object and its electrical properties. In particular, we show that dendrites present an asymmetric non-linear behavior due to the volume of polymer that increases the capacitance of the device albeit not participating in conduction. Moreover, because of the ionoelectronic coupling that exists within an electrolyte, two active devices working concomita ntly will influence one another. We demonstrate that it is a blessing for the realization of computing tasks, such as logic, and that it can also be used to program the dendrites into non-volatile conductance states. Therefore, dendritic networks could c onstitute a new building block for non-conventional information processing that fits into the larger framework of brain-inspired computing.

Abstract: Neuromorphic computing and engineering is capitalizing heavily on the new physical properties offered by nantechnologies to engineer biological processes. At the frontiers in between bio-mimetism and bio-inspiration, various solutions have been proposed for synaptic plasticity or neuronal features based on discrete memory elements, bistable switches or transistors circuits. One missing element that has been missing in the neuromorphic toolbox is the ability to reproduce the complex 3D interconnections o bserved in biological neural networks. Here, we propose to take advantage of bipolar electropolymerization of PEDOT dendritic fibers to reproduce the ability of neural networks to generate complex topologies. The electropolymerization mechanism is used t o realize structural plasticity based on Hebbian-like plasticity rules. We explore how such bottom-up process can find optimal topologies for specific computing tasks. We demonstrate that such optimal topologies results in a drastic reduction of intercon nects for classification and reconstruction tasks, thus offering an interesting option for neural network design.

Abstract: In electronics, circuits are predefined for a lifetime. Fabricating always the same with highly stable components is a real advantage for mass production. However when technologies are no longer up to date, electronic devices can only be discarded, poorl y recycled, because they cannot adapt. Living organisms, on the other hand, are constantly evolving. They learn and interact with their environment, and when doing so, brain-neurons or tree-roots grow with appearent disorder but a surprising way to learn to exploit local ressources. The way they branch out can be described as morphogenesis and is the symbol of a natural intelligence, too complex to modelize. While conventional electronics seeks to eliminate disorder and variability, we hypothesize that it is possible to make use of it in a novel electronics that uses electrochemistry to mimic biological processes for adaptation. In this study, we will discuss on morphogenesis of a conducting polymer (PEDOT:PSS) through the AC electropolymerization of E DOT in water. The dendritic objects exhibit various morphologies, differing in their thicknesses and number of branches. Their growth mechanism involves diffusion and electromigration of charged species within the solution. We also present the first resu lts characterizing a connection of those objects in the frequency domain, where various dynamics can be observed due to specific mechanisms at the different interfaces. The electropolymerization of EDOT offers an inexpensive way to grow directed connecti ons with a specific impedance to connect components in a system by voltage activation. It could be used to address the limits of the current electronics in terms of cost and flexibility while taking a form that is closer to what can be found in nature.

Abstract: In the quest to change the way we envision computing, the ability to create connections in between computing nodes on demand offers the very exciting possibility to explore bottom-up strategies when it comes to thinking the design of electronic devices a nd circuits. Recently, the growth of conductive polymer fibers via electropolymerization (most notably poly(3,4-ethylenedioxythiophene) doped with poly(styrenesulfonate), abbreviated as PEDOT:PSS) was employed in the realm of neuromorphic hardware, first as a way to tune the resistance of a connection, and then to perform computing tasks, such as biosignal classification trough reservoir computing, thus establishing the advantages of the method. In this work, we propose to demonstrate that electropolyme rization is a useful tool for the creation of sensors that are able to realize computing tasks in an aqueous environment. By taking advantage of the morphology of the fibers, we show that PEDOT:PSS dendrites can discriminate between different types of vo ltage pulses emitted in their vicinity by a local gate electrode, thus performing in materio classification. In addition, we discuss the growth of networks of polymer fibers on 2D substrates. The ability to create structures that cover several microelect rodes allows us to study the behavior of the whole system instead of a single dendrite. Complex interactions through iono-electronic coupling arise when several fibers are interconnected. These mechanisms (e.g. self- / inter-gating, ionic relaxation,...) offer new possibilities for computing signals and could potentially open the way for new applications.

Abstract: During recent years, neuromorphic engineering has gained significant attraction due to its endeavor to reproduce attractive brain features such as high computational efficiency, low power consumption, functional and structural adaptability onto hardware devices for competitive computing development. Currently, most of these implementations are achieved with either standard silicon-based technologies (complementary metal-oxide-semiconductor) or more emerging material and devices (iono-electronic material s and memristor devices). Mainly, these technologies are produced by means of top-down approaches. In contrast, brain computing largely relies on self-assembling processes to interconnect cells and form pathways for neural networks communication. Thus, t o benefit more from neuromorphic features, there is a strong need to explore such devices that can rely on the same bottom-up approach as in the brain. Promising solutions for this question are gathered in organic electronic materials, more precisely in organic mixed ionic electronic conductors. This study is focused on the development of bottom-up fashioned 3D organic devices that are able to mimic biological neural network branching. By employing Alternating-Current (AC) bipolar electropolymerization, we show how one can synthesize polymer dendritic structures and tune its morphologies depending on various applied AC signals . Additionally, we show how dendritic devices may exhibit synaptic plasticity properties (short-term and long-term memory effec t). Finally, we demonstrate implementation of structural plasticity through spike-event activation AC-electropolymerization and the possibility to modify the weight of obtained dendritic connections with respect to spike rate intensity of the applied sig nals.

Abstract: The development of electronic devices for neurosensing is leading to fundamental discoveries in communication setups for interfacing and computing the brain's electrical activity that is still a demanding task in the 21st century. One of the greatest cha llenges for efficient neurosensing is to ensure that detection/transduction between biochemically rich systems and tools is fully mastered to reliably gather relevant information. In extracellular devices such as microelectrode arrays (MEAs), the discord ance lies at the interface between ions and the electrodes. Engineering chemically/morphologically the electrode's materials by decreasing its impedance, improving its affinity with neurons, and boosting its biocompatibility ensures better cell/electrode interface conditions to find the right materials that detect ionic signals from neurons and transduce them into electronic signals with the lowest information loss. Hence, the use of conducting polymers (PEDOT) has emerged for optimizing the performance of microelectrodes in neurosensing due to its mixed ionic electronic conduction, biocompatibility and low impedance. In parallel to the development of passive microelectrode, organic electrochemical transistors (OECTs) have received lots of attention in the biosensing field since they exhibit high coupling with cells and signal amplification. Notably, the transconductance represents an important parameter that depends on geometrical and material parameters that rules largely OECTs performances in biose nsing. In this direction, we explore the use of EDOT electropolymerization to tune post-fabrication material and geometrical parameters of passive microelectrodes for optimizing the cell/electrode interface by decreasing its impedance and improving its a ffinity with neurons (increasing the resistance "Rseal" that represents the cell/electrode cleft). For electropolymerized PEDOT MEAs, we demonstrate long term and stable extracellular recording of primary cortical neurons with a record signal-to-noise ra tio (SNR) up to 37 dB (with ultra-low noise down to 2.1 μV RMS). Secondly, for active sensing with OECTs, this strategy exploits the concept of adaptive sensing where both transconductance and impedance are tuned simultaneously or independently. This approach shows an improvement of OECTs transconductance by 150-percent, volumetric capacitance by 300-percent, and a reduction in array's variability by 60-percent in comparison with standard spin-coated OECTs. The cytotoxicity of the electropolymerized EDOT was assessed for primary neural cells culture and no detrimental effect of electropolymerized EDOT on cell viability was observed. To extract the impedance and transconductance values for both MEAs and OECTs, we combine DC electrical measurements w ith electrochemical impedance spectroscopy (EIS). To show the cell/electrode morphology and neurite outgrowth to electropolymerized microelectrodes, Scanning Electron Microscopy (SEM) was performed. To correlate the morphological changes of the material with the enhancement of its electrical and electrochemical performances, Atomic Force Microscopy (AFM) in liquid and Raman Spectroscopy were achieved. Finally, in-vitro extracellular recorded signals from entorhinal cortex cultured slices and primary cor tical neurons using both MEAs and OECTs are presented. The key novelty of this technique is to propose a post-fabrication material engineering technique that can be used to optimize both passive (MEAs) and active (OECTs) devices for extracellular recordi ng and promote new exploratory sensing strategies to ensure high quality neurosensing alternatives.

Abstract: Iono-electronic materials and devices are suscitating lots of interest from both bio-electronics and neuromorphic research communities. In the one hand, iono-electronic materials are offering attractive features such as bio-compatibility, water environme nt operation and efficient ionic to electronic signals transduction. In the other hand, functional devices based on such materials (organic electrochemical transistors, for instance) have shown multiple neuromorphic features from synaptic plasticity to d endritic integration. This talk will present how electropolymerization of PEDOT:PSS materials can be used in both a bio-electronic and a neuromorphic perspective. Electropolymerization of OECT sensors can indeed be advantageously used for optimizing / tu ning the iono-electronic responses of organic electrochemical transistors, thus paving the way to plastic electrophysiological sensors. Notably, we will show how transconductance and volumetric capacitance are evolving with potenstiostatic electropolymer ization. Secondly, bipolar AC electropolymerization can be used to engineer dendritic-like fibers of PEDOT:PSS. Such unconventional structures can implement various neuromorphic concept such as structural plasticity and synaptic plasticity. Finally, comp uting task taking advantage of both electropolymerized sensors and neuromorphic computing will present temporal patterns classification with reservoir computing approach.

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: The recent progress in the extracellular microelectrode arrays (MEAs) have greatly improved our ability to probe cellular electrophysiological activities. Nevertheless, passive MEAs are subject to small signal-to-noise ratio and small potential detection . Recently, organic electrochemical transistors (OECTs) have been identified as a promising device architecture to improve extracellular potentials recording in electroactive cells culture both in-vitro and in-vivo. In addition to unique properties of in terest for electrophysiology such as biocompatibility, transparency and flexibility, OECTs operating principle is based on the transduction of ionic currents in the biological medium into electronic currents in the organic semiconductor (e.g. PEDOT:PSS) via electrochemical coupling. The transconductance represents an important figure of merit of OECTs and depends on geometrical and material parameters that rules largely OECTs performances for sensing electrophysiological signals. However, as an organic electronic technology, larger device-property distributions are often encountered with respect to the one of metal- or inorganic-based technologies, inherent to the very nature of the soft organic materials involved in the OECTs transduction process. Her e, we explore the possibility to tune post-fabrication material and geometrical parameters of OECTs with electropolymerization of EDOT. We show that this strategy can be used to simultaneously improve OECT transconductance and its geometrical capacitance . The addressed OECT chips were micro-fabricated on a glass substrates with spin coated PEDOT:PSS. Electropolymerization of EDOT on top of spin-coated PEDOT:PSS was carried on with both fix voltage and ramp voltage techniques. A detail impedance analysis was performed during OECTs functionalization. DC electrical characterizations was used to correlate the transconductance and capacitance tuning due to electropolymerization and to assess device performances improvements. Scanning Electron Microscopy (SE M) was used to correlate morphological changes due to electropolymerization with the enhancement in the transconductance and capacitance of the OECTs. Finally, we performed bio-compatibility assessment between primary neural cells culture and the differe nt possible monomers used for electropolymerization to evaluate the possibility to improve affinity between cultured neurons and electropolymerized materials. The key novelty of this material engineering technique is to propose a promising method for tun able OECTs sensors development. For instance, this back-end-of-line tuning technique can reduce chip variability in terms of performance yield and bring OECTs technology to the next maturity level. Furthermore, such flexibility can enable matching the el ectrochemical impedance of the device to the one of the cells, and in the future promote exploratory sensing missions, merging brain-inspired information processing with neuro-sensing.},

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.

Abstract: Self-Assembled Monolayers (SAMs) on gold were successfully implemented to passivate gold contact organic cell stimulating and sensing transistors. The results show the devices to be greatly insulated from water. This breakthrough is proposed as a technol ogical improvement for bio-stimulation and bio-sensing organic field-effect devices.

Abstract: In this work we report the stimulation of astrocytic calcium signalling using an organic cell sensing and stimulating transistor (O-CST). We demonstrate that astroglial cells can adhere and proliferate on these devices giving us the possibility to stimul ate bioelectrical activity in this type of cells. By the use of a microfluorimetric calcium imaging approach we show that our device is able to evoke an increase in intracellular calcium levels. This research opens a new path in the study of glial cells and their bioelectrical activity.

Abstract: