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: CLD (Chlordecone) is a persistent organic pollutant (POP) of great concerns due toxicity and environmental persistence. To this end, we study organic electrochemical transistors (OECT) featuring biological probes to detect CLD. Here, we report a strategy for immobilizing a D09 VHH receptor, which detects CLD-biot in water. WCA (Water Contact Angle), AFM, XPS and FTIR confirm the successful immobilization of D09 on a surface operating as gate electrode sensitive to the presence of CLD-biot in the transfe r characteristic.

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 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: Over the past few years, organic electronics - and especially organic mixed ionic electronic conductors (OMIECs) - has taken bio sensing and neuromorphic applications to a whole new level. However, one of the major limitations of the mainstream technolog ies today is that electronic circuits need to be pre-shaped according to the intended use and the expected outcome. This top-down approach, far from being flexible/adaptive, does not really make the most of the resources at hand, as it is hard to predict precisely where cells will be located. To counter that, we can either choose to increase the density and the number of electrodes, so that the entire area would be mapped, or shift from a top-down to a bottom-up approach which would allow for a more enl ightened decision-making process. Recently, the electrodeposition of PEDOT:PSS has been explored as a novel technique to grow conducting polymer films and fibers on non-conductive substrates. The work of Janzakova and coworkers took that concept a step f urther by using electropolymerization of EDOT as a way to create freestanding dendritic-like conductive fibers in a 3D environment, paving the way for in operando material modification, and in fine bottom-up fabrication routes that would be more adaptive and allow for more flexibility. Moreover, it was lately showed that these objects could work as Organic Electrochemical Transistors (OECTs). Here, we explore the possibility of growing dendritic-like PEDOT fibers on Multielectrode Arrays (MEAs) via elec tropolymerization of EDOT. Electrophysiological measurements are based on the capacitive coupling between cells and the electrode material. In comparison with local electrodes, the dendritic objects present spatially distributed impedance due to the exte nsions of their dendritic branches interacting with the biological environment. We investigate the relation between morphology and impedance in these dendritic-like fibers by using a non-conventional Electrochemical Impedance Spectroscopy (EIS) setup tha t will allow us to apply a potential difference between the two ends of the dendrites, thus studying how biasing them can affect their behavior. Moreover, it appears that dendritic fibers can be considered both as passive electrodes as well as active dev ices. We explore the use of these two strategies in the context of electrophysiological measurements. Finally, the ability to record biological signals results from the interaction between cells and an electrode. Unconventional objects such as dendrites present spatio-temporal filtering properties that could affect the recording of such signals. We investigate how tuning the impedance of a dendrite might be used to record efficiently bio-signals.

Abstract: The development of electronic devices such as microelectrode arrays (MEAs), used to record extracellularly simultaneous electrical activity of large populations of neurons is blooming. To enhance the quality of the recordings, the use of electrode made o f conducting polymer such as PEDOT has recently emerged for optimizing the performance of microelectrodes due to its mixed ionic electronic conduction, biocompatibility and low impedance. However, the extent to which these new interfaces can help the alg orithmic pipelines of spike sorting, i.e. turning extracellular potentials into individual spike trains remains unexplored. To address this issue, we checked if the physical positions of the neurons could be reliably inferred from extracellular electrica l recordings obtained by MEAs, and thus be exploited by downstreams spike sorting algorithms. To do so, we combine high resolution images of neuronal tissues and dense recordings performed via high performant electropolymerized electrodes based MEAs. Fir stly, we report the use of EDOT electropolymerization to tune post-fabrication material and geometrical parameters of passive microelectrodes. The process optimizes the cell/electrode interface by decreasing its impedance and improving its affinity with neurons: results demonstrate a better biocompatibility and improved signal-to-noise ratio (SNR) (up to 40 dB). Thanks to the higher SNR, we were able to detect more cells in comparison with gold electrodes from the same neural network by using spike sort ing. Hence, the higher number of cells detected will lead into more accurate analysis of the localization of the active neurons on top of MEA. Secondly, by using these high performant MEAs, we investigated the possibility to accurately estimate the posit ions of the neurons solely from extracellular recordings by studying the correlation between electrical activity (obtained via spike sorting), optical imaging (Fluorescent) and Scanning Electron Microscopy (SEM) of neural networks cultured on MEAs. By us ing the SpykingCircus software to spike sort the extracellular recordings, we estimated the positions of the neurons either by using the center of mass of their electrical signatures, or by inferring the positions assuming cells would behave as monopoles . By superposition of the fluorescent and the SEM images, we compared the observed physical positions of the neurons with the ones predicted by the two aforementioned methods. This approach showed the high accuracy of the monopole hypothesis compared to the center of mass. In this work, we showed how the use of a material engineering technique for optimizing state of art MEAs can enhance the quality of the recordings. Hence, the correlation of these high quality recordings with optical imaging paves the way towards new algorithmic possibilities for spike sorting algorithms that could make use of a more reliable estimation of neuronal positions.

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:

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.},