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: Identifying relevant machine-learning features for multi-sensing platforms is both an applicative limitation to recognize environments and a necessity to interpret the physical relevance of transducers' complementarity in their information processing. Pa rticularly for long acquisitions, feature extraction must be fully automatized without human intervention and resilient to perturbations without increasing significantly the computational cost of a classifier. In this study, we investigate the relative r esistance and current modulation of a 24-dimensional conductimetric electronic nose, which uses the exponential moving average as a floating reference in a low-cost information descriptor for environment recognition. In particular, we identified that dep ending on the structure of a linear classifier, the 'modema' descriptor is optimized for different material sensing elements' contributions to classify information patterns. The low-pass filtering optimization leads to opposite behaviors between unsuperv ised and supervised learning: the latter one favors longer integration of the reference, allowing to recognize five different classes over 90%, while the first one prefers using the latest events as its reference to cluster patterns by environment nature . Its electronic implementation shall greatly diminish the computational requirements of conductimetric electronic noses for on-board environment recognition without human supervision.

Abstract: Neuromorphic computing is an exciting and rapidly growing field that aims to create computing systems that can replicate the complex and dynamic behavior of the human brain. Organic electrochemical transistors (OECTs) have emerged as a promising tool for developing such systems due to their unique bioelectronic properties. In this paper, we present a novel approach for signal classification using an OECT array, which exhibits multifunctional bioelectronic functionality similar to neurons and synapses li nked through a global medium. Our approach takes advantage of the intrinsic device variabilities of OECTs to create a reservoir network with variable neuron-time constants and synaptic strengths. We demonstrate the effectiveness of our approach by classi fying surface-electromyogram (sEMG) signals into three hand gesture categories. The OECT array performs efficient signal acquisition by feeding signals through multiple gates and measuring the response to a group of OECTs with a global liquid medium. We compare the performance of our approach with and without projecting the input on OECTs and observe a significant increase in classification accuracy from 40% to 68%. We also examined how the classification performance is affected by different selection s trategies and numbers of OECTs used. Finally, we developed a spiking neural network-based simulation that mimics the OECTs array and found that OECT-based classification is comparable to the spiking neural network-based approach. Our work paves the way f or the next generation of low-power, real-time, and intelligent biomedical sensing systems.

Abstract: Neural networks are powerful tools for solving complex problems, but finding the right network topology for a given task remains an open question. Biology uses neurogenesis and structural plasticity to solve this problem. Advanced neural network algorith ms are mostly relying on synaptic plasticity and learning. The main limitation in reconciling these two approaches is the lack of a viable hardware solution that could reproduce the bottom-up development of biological neural networks. Here, we show how t he dendritic growth of PEDOT:PSS-based fibers through AC electropolymerization can implement structural plasticity during network development. We find that this strategy follows Hebbian principles and is able to define topologies that leverage better com puting performances with sparse synaptic connectivity for solving non-trivial tasks. This approach is validated in software simulation, and offers up to 61% better network sparsity on classification and 50% in signal reconstruction tasks.

Abstract: Multivariate data analysis and machine-learning classification become popular tools to extract features without physical models for complex environments recognition. For electronic noses, time sampling over multiple sensing elements must be a fair compro mise between a period sufficiently long to output a meaningful information pattern, and sufficiently short to minimize training time for practical applications. Particularly when reactivity's kinetic differs from thermodynamic in sensitive materials, fin ding the best compromise to get the most from data is not obvious. Here, we investigate on the influence of data acquisition to improve or alter data clustering for molecular recognition on a conducting polymer electronic nose. We found out that waiting for sensing elements to reach their steady state is not required for classification, and that reducing data acquisition down to the first dynamical information suffice to recognize molecular gases by principal component analysis with the same materials. Especially for online inference, this study shows that a good sensing array is no array of good sensors, and that new figure-of-merits shall be defined for sensing hardware aiming machine-learning pattern-recognition rather than metrology.

Abstract: Microelectrode Arrays (MEAs) are popular tools for in vitro extracellular recording. They are often optimized by surface engineering to improve affinity with neurons and guarantee higher recording quality and stability. Recently, PEDOT:PSS has been used to coat microelectrodes due to its good biocompatibility and low impedance, which enhances neural coupling. Herein, we investigate on electro-co-polymerization of EDOT with its triglymated derivative to control valence between monomer units and hydrophil ic functions on a conducting polymer. Molecular packing, cation complexation, dopant stoichiometry are governed by the glycolation degree of the electro-active coating of the microelectrodes. Optimal monomer ratio allows fine-tuning the material hydrophi licity and biocompatibility without compromising the electrochemical impedance of microelectrodes nor their stability while interfaced with a neural cell culture. After incubation, sensing readout on the modified electrodes shows higher performances with respect to unmodified electropolymerized PEDOT, with higher signal-to-noise ratio (SNR) and higher spike counts on the same neural culture. Reported SNR values are superior to that of state-of-theart PEDOT microelectrodes and close to that of state-of-t he-art 3D microelectrodes, with a reduced fabrication complexity. Thanks to this versatile technique and its impact on the surface chemistry of the microelectrode, we show that electro-co-polymerization trades with manycompound properties to easily gathe r them into single macromolecular structures. Applied on sensor arrays, it holds great potential for the customization of neurosensors to adapt to environmental boundaries and to optimize extracted sensing features.

Abstract: Extracting relevant data from real-world experiments is often challenging with intrinsic materials and device property dispersion, such as in organic electronics. However, multivariate data analy-sis can often be a mean to circumvent this and to extract more information when larger datasets are used with learning algorithms instead of physical models. Here, we report on identifying rele-vant information descriptors for organic electrochemical transistors (OECTs) to classify aqueous electrolytes by ionic composition. Applying periodical gate pulses at different voltage magnitudes, we extracted a reduced number of nonredundant descriptors from the rich drain-current dynam-ics, which provide enough information to cluster electrochemical data by principal component analysis between Ca2+-, K+-, and Na+-rich electrolytes. With six current values obtained at the ap-propriate time domain of the device charge/discharge transient, one can identify the cationic iden-tity of a locally probed transient current wit h only a single micrometric device. Applied to OECT-based neural sensors, this analysis demonstrates the capability for a single nonselective de-vice to retrieve the rich ionic identity of neural activity at the scale of each neuron individually when lea rning algorithms are applied to the device physics.

Abstract: Recently, the development of electronic devices to extracellularly record the simultaneous electrical activities of numerous neurons has been blooming, opening new possibilities to interface and decode neuronal activity. In this work, we tested how the u se of EDOT electropolymerization to tune post-fabrication materials could optimize the cell/electrode interface of such devices. Our results showed an improved signal-to-noise ratio, better biocompatibility, and a higher number of neurons detected in com parison with gold electrodes. Then, using such enhanced recordings with 2D neuronal cultures combined with fluorescent optical imaging, we checked the extent to which the positions of the recorded neurons could be estimated solely via their extracellular signatures. Our results showed that assuming neurons behave as monopoles, positions could be estimated with a precision of approximately tens of micrometers.

Abstract: This study presents the development of an electronic nose comprising eight homemade sensors with pure P3HT and doped with different materials. The objective is to electronically identify the gases exposed on these sensors and evaluate the accuracy of tar get gas classification. The resistance variation for each sensor is measured over time and the collected data were processed by three different identification techniques as following: principal component analysis (PCA), linear discriminate analysis (LDA) , and nearest neighbor analysis (kNN). The merit factor for the analysis is the relative modulation of the resistance is very important and computationally gives different results. In addition, the fact that we have sensors made with innovative materials where the reproducibility of the response for the same material can be a constraint in the recognition. In contrast, we have shown that despite the lack of reproducibility for the same material on two different sensors and despite the instability during the ten last sec, we have good recognition rates and we can even say which algorithm is better. It is noted that the LDA is the most reliable and efficient method for gas classification with a prediction accuracy equal to 100%, whereas it reach 93.52% a nd 73.14% for PCA and kNN, respectively, for other techniques for 40% of training dataset and 60% of testing dataset.

Abstract: The brain capitalizes on the complexity of both its biochemistry for neurons to encode diverse pieces of information with various neurotransmitters and its morphology at multiple cales to route different pathways for neural interconnectivity. Conducting polymer dendrites can show similar features by differentiating between cations and anions thanks to their charge accumulation profile and the asymmetry in their dendriticity that allows projecting spike signals differently. Here, we exploit such mimicry for in materio classification of bursting activity and investigate, in phosphate buffered saline, the capability of such object to sense bursts of voltage pulses of 100 mV amplitude, emitted by a local gate in the vicinity of the dendrite. The dendrite i ntegrates the different activities with a fading memory time window that is characteristic of both the polarity of the spikes and the temporality of the burst. By this first demonstration, the 'material-object' definitely shows great potential to be a no de halfway between the two realms of brain and electronic communication.

Abstract: Electropolymerization is a bottom-up materials engineering process of micro/nano-scale that utilizes electrical signals to deposit conducting dendrites morphologies by a redox reaction in the liquid phase. It resembles synaptogenesis in the brain, in whi ch the electrical stimulation in the brain causes the formation of synapses from the cellular neural composites. The strategy has been recently explored for neuromorphic engineering by establishing link between the electrical signals and the dendrites' s hapes. Since the geometry of these structures determines their electrochemical properties, understanding the mechanisms that regulate polymer assembly under electrically programmed conditions is an important aspect. In this manuscript, we simulate this p henomenon using mesoscale simulations, taking into account the important features of spatial-temporal potential mapping based on the time-varying signal, the motion of charged particles in the liquid due to the electric field, and the attachment of parti cles on the electrode. The study helps in visualizing the motion of the charged particles in different electrical conditions, which is not possible to probe experimentally. Consistent with the experiments, the higher AC frequency of electrical activities favors linear wire-like growth, while lower frequency leads to more dense and fractal dendrites' growth, and voltage offset leads to asymmetrical growth. We find that dendrites' shape and growth process systematically depend on particle concentration an d random scattering. We discover that the different dendrites' architectures are associated with different Laplace and diffusion fields, which govern the monomers' trajectory and subsequent dendrites' growth. Such unconventional engineering routes could have a variety of applications from neuromorphic engineering to bottom-up computing strategies.

Abstract: Although materials and processes are different from biological cells', brain mimicries led to tremendous achievements in parallel information processing via neuromorphic engineering. Inexistent in electronics, we emulate dendritic morphogenesis by electr opolymerization in water, aiming in operando material modification for hardware learning. Systematic study of applied voltage-pulse parameters details on tuning independently morphological aspects of micrometric dendrites': fractal number, branching degr ee, asymmetry, density or length. Growths time-lapse image processing shows spatial features to be dynamically dependent, and expand distinctively before and after conductive bridging with two electro-generated dendrites. Circuit-element analysis and imp edance spectroscopy confirms their morphological control in temporal windows where growth kinetics is finely perturbed by the input frequency and duty cycle. By the emulation of one's most preponderant mechanisms for brain's long-term memory, its impleme ntation in vicinity of sensing arrays, neural probes or biochips shall greatly optimize computational costs and recognition required to classify high-dimensional patterns from complex environments.

Abstract: Organic electrochemical transistors are considered today as a key technology to interact with a biological medium through their intrinsic ionic-electronic coupling. In this paper, the authors show how this coupling can be finely tuned (in operando) post- microfabrication via the electropolymerization technique. This strategy exploits the concept of adaptive sensing where both transconductance and impedance are tunable and can be modified on-demand to match different sensing requirements. Material investi gation through Raman spectroscopy, atomic force microscopy, and scanning electron microscopy reveals that electropolymerization can lead to a fine control of poly(3,4-ethylenedioxythiophene) (PEDOT) microdomains organization, which directly affects the i ono-electronic properties of organic electrochemical transistors (OECTs). They further highlight how volumetric capacitance and effective mobility of PEDOT:polystyrene sulfonate influence distinctively the transconductance and impedance of OECTs. This ap proach shows to improve the transconductance by 150% while reducing their variability by 60\% in comparison with standard spin-coated OECTs. Finally, they show how the technique can influence voltage spike rate hardware classification with direct interes t in bio-signals sorting applications.

Abstract: One of the major limitations of standard top-down technologies used in today's neuromorphic engineering is their inability to map the 3D nature of biological brains. Here, it is shown how bipolar electropolymerization can be used to engineer 3D networks of PEDOT:PSS dendritic fibers. By controlling the growth conditions of the electropolymerized material, it is investigated how dendritic fibers can reproduce structural plasticity by creating structures of controllable shape. Gradual topologies evolution is demonstrated in a multielectrode configuration. A detailed electrical characterization of the PEDOT:PSS dendrites is conducted through DC and impedance spectroscopy measurements and it is shown how organic electrochemical transistors (OECT) can be re alized with these structures. These measurements reveal that quasi-static and transient response of OECTs can be adjusted by controlling dendrites' morphologies. The unique properties of organic dendrites are used to demonstrate short-term, long-term, an d structural plasticity, which are essential features required for future neuromorphic hardware development.},

Abstract: In this work, we demonstrate P3HT (poly 3-hexylthiophene) organic rectifier diode both in rigid and flexible substrate with a rectification ratio up to 106. This performance has been achieved through tuning the work function of gold with a self-assembled monolayer of 2,3,4,5,6-pentafluorobenzenethiol (PFBT). The diode fabricated on flexible paper substrate shows a very good electrical stability under bending tests and the frequency response is estimated at more than 20 MHz which is sufficient for radio frequency identification (RFID) applications. It is also shown that the low operating voltage of this diode can be a real advantage for use in a rectenna for energy harvesting systems. Simulations of the diode structure show that it can be used at GSM an d Wi-Fi frequencies if the diode capacitance is reduced to a few pF and its series resistance to a few hundred ohms. Under these conditions, the DC voltages generated by the rectenna can reach a value up to 1 V.

Abstract: Organic semiconductors have enough molecular versatility to feature chemo-specific electrical sensitivity to large families of chemical substituents via different intermolecular bonding modes. This study demonstrates that one single conducting polymer ca n be tuned to either discriminate water-, ethanol- or acetone-vapors, on demand, by changing the nature of its dopant. Seven triflate salts differ from mild to strong p-dopant on poly(3-hexylthiophene) sensing micro-arrays. Each material shows a pattern of conductance modulation for the polymer which is reversible, reproducible, and distinctive of other gas exposures. Based on principal component analysis, an array doped with only two different triflates can be trained to reliably discriminate gases, wh ich re-motivates using conducting polymers as a class of materials for integrated electronic noses. More importantly, this method points out the existence of tripartite donor-acceptor charge-transfer complexes responsible for chemospecific molecular sens ing. By showing that molecular acceptors can have duality to p-dope semiconductors and to coordinate donor gases, such behavior can be used to understand the role of frontier orbital overlapping in organic semiconductors, the formation of charge-transfer complexes via Lewis acid-base adducts in molecular semiconductors.

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- assembled 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 106 has been achieved. Their high frequency respons e 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 de vice applications or for GSM band energy harvesting in low-cost IoT objects.

Abstract: In this study, we present the microfabrication and characterization of a transparent microelectrode array (MEA) system based on PEDOT:PSS for electrophysiology. The influence of the PEDOT:PSS electrode dimensions on the impedance was investigated and the stability over time under physiological environment was demonstrated. A very good transparency value was obtained by our system displaying one of the best impedance and transmittance values when compared to other transparent MEA. After biocompatibility validation, we successfully recorded spontaneous neuronal activity of primary cortical neurons cultured over 4 weeks on the transparent PEDOT:PSS electrodes. This work shows that microelectrodes composed of PEDOT:PSS are very promising as a new tool for both electrophysiology and fluorescence microscopy studies on neuronal cell cultures.

Abstract: Simultaneously optimizing performances, processability and fabrication cost of organic electronic materials is the continual source of compromise hindering the development of disruptive applications. In this work, we identified a strategy to achieve reco rd conductivity values of one of the most benchmarked semiconducting polymers by doping with an entirely solution-processed, water-free and cost-effective technique. High electrical conductivity for poly(3-hexylthiophene) up to 21 S/cm has been achieved, using a commercially available electron acceptor as both a Lewis acid and an oxidizing agent. While we managed water-free solution-processing a three-time higher conductivity for P3HT with a very affordable/available chemical, near-field microscopy reve als the existence of concentration-dependent higher-conductivity micro-domains for which furthermore process optimization might access to even higher performances. In the perpetual quest of reaching higher performances for organic electronics, this work shall greatly unlock applications maturation requiring higher-scale processability and lower fabrication costs concomitant of higher performances and new functionalities, in the current context where understanding the doping mechanism of such class of ma terials remains of the greatest interest.

Abstract: The increasing popularity of machine learning solutions puts increasing restrictions on this field if it is to penetrate more aspects of life. In particular, energy efficiency and speed of operation is crucial, inter alia in portable medical devices. The Reservoir Computing (RC) paradigm poses as a solution to these issues through foundation of its operation - the reservoir of states. Adequate separation of input information translated into the internal state of the reservoir - whose connections do not need to be trained - allow to simplify the readout layer thus significantly accelerating the operation of the system. In this paper, the theoretical basis of RC was first described, followed by a description of its individual variants, their development and state-of-the-art applications in chemical sensing and metrology: detection of impedance changes and ion sensing. Presented results indicate applicability of reservoir computing for sensing and validating the SWEET algorithm experimentally.

Abstract: In the recent years, the organic electrochemical transistors (OECT) have attracted considerable attention for biosensing applications due to the biocompatibility of their materials and their low operating voltages. Upon exposure to an electrolyte, the dr ain current becomes ion-dependent. This can be exploited for sensing ion applications. To facilitate the process of designing such powerful ion sensing devices one needs the ability to simulate the transient dynamical behavior of many OECT elements conne cted in a network. We have developed a generic theoretical model of the OECT element that can be used for such purposes. Our OECT element resembles a typical FET three-port element with the response function parameterized with an additional time-dependen t variable, T, which describes how far the element operates from the stationary state. We have constructed a dynamical equation that describes how T changes in time when the element is exposed to arbitrary external voltages. This makes the element model highly interoperable with generic electrical circuit simulators. We provide an example of possible numerical implementation using the modified nodal analysis. We tested the underlying theoretical assumptions by comparing model predictions with experiment al data and found a reasonable agreement. Our model describes the typical current spikes observed in the literature.

Abstract: Organic electrochemical transistors offer powerful functionalities for biosensors and neuro-inspired electronics, with still much to understand on the time-dependent behavior of this electrochemical device. Here, we report on distributed-element modeling of the impedance of such micro-fabricated device, systematically performed under a large concentration variation for KCl_(aq) and CaCl_2(aq). We propose a new model which takes into account three main deviations to ideality, that wer e systematically observed, caused by both the materials and the device complexity, over large frequency range (1 Hz-1 MHz). More than introducing more freedom degree, the introduction of these non-redundant parameters and the study of their behaviors as function of the electrolyte concentration and applied voltage give a more detailed picture of the OECT working principles. This optimized model can be further useful for improving OECT performances in many applications (e.g. biosensors, neuro-inspired de vices, ...) and circuit simulations.

Abstract: Neuromorphic computing and engineering has been the focus of intense research efforts that have been intensified recently by the mutation of Information and Communication Technologies. In fact, new computing solutions and new hardware platforms are expec ted to emerge to answer to the new needs and challenges of our societies. In this revolution, lots of candidates' technologies are explored and will require leveraging of the pros and cons. In this perspective paper belonging to the special issue on neur omorphic engineering of Journal of Applied Physics, we focus on the current achievements in the field of organic electronics and the potentialities and specificities of this research field. We highlight how unique material features available through orga nic materials can be used to engineer useful and promising bio-inspired devices and circuits. We also discuss the opportunities that organic electronics offer for future research directions in the neuromorphic engineering field.

Abstract: Based on bottom-up assembly of highly variable neural cells units, the nervous system can reach unequalled level of performances with respect to standard materials and devices used in microelectronic. Reproducing these basic concepts in hardware could po tentially revolutionize materials and device engineering which are used for information processing. Here, an innovative approach that relies on both iono-electronic materials and intrinsic device physics to show pattern classification out of a 12-unit bi osensing array is presented. The reservoir computing and learning concept to demonstrate relevant computing based on the ionic dynamics in 400 nm channel-length organic electrochemical transistor is used. It is shown that this approach copes efficiently with the high level of variability obtained by bottom-up fabrication using a new electropolymerizable polymer, which enables iono-electronic device functionality and material stability in the electrolyte. The effect of the array size and variability on t he performances for a real-time classification task paving the way to new embedded sensing and processing approaches is investigated.

Abstract: In this work, we propose a strategy to sense quantitatively and specifically cations, out of a single organic electrochemical transistor (OECT) device exposed to an electrolyte. From the systematic study of six different chloride salts over 12 different concentrations, we demonstrate that the impedance of the OECT device is governed by either the channel dedoping at low frequency and the electrolyte gate capacitive coupling at high frequency. Specific cationic signatures, which originates from the diffe rent impact of the cations behavior on the poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) polymer and their conductivity in water, allow their discrimination at the same molar concentrations. Dynamic analysis of the device impedance at different frequencies could allow the identification of specific ionic flows which could be of a great use in bioelectronics to further interpret complex mechanisms in biological media such as in the brain.

Abstract: We report on hydrazine-sensing organic electrochemical transistors (OECTs) with a design consisting of 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, poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS)-based OECTs have been studied as aqueous sensors that are specifically sensitive to the lethal hydrazine molecule. These amperometric sensors have many re levant 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 nine other water-soluble common analytes, the capability to e ntirely recover its base signal after water flushing, and a very low operation voltage. The specificity for hydrazine to be sensed by our OECTs is caused by its catalytic oxidation at the gate electrode, and enables an increase in the output current modu lation 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 governing 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-talk, m icrometric gate-integrated radial OECTs shall contribute to the diminishing of the readout invasiveness and therefore further promote the development of OECT biosensors.

Abstract: Ten new efficient p-dopants for conductivity doping of organic semiconductors for OLEDs are identified. The key advantage of the electrophilic tris(carboxylato) bismuth(III) compounds is the unique low absorption of the resulting doped layers which promo tes the efficiency of OLED devices. The combination of these features with their low fabrication cost, volatility, and stability, make these materials very attractive as dopants in organic electronics.