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.
Pecqueur S.*, Vuillaume D., Crljen Ž., Lončarić I., Zlatić V.*
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.
Przyczyna D., Pecqueur S., Vuillaume D., Szaciłowski K.*
Abstract: Organic electrochemical transistors (OECTs) offer a powerful functionality for both sensing and neuro-inspired electronics with still much to understand on their time-dependent behavior. OECTs based on PEDOT:PSS conducting polymer h ave revealed two distinctive operation regimes of a device: a low frequency and a higher frequency regimes dominated by the conductance of the polymer and of the gating electrolyte, respectively. However, the systematically observed non-idealities in t he impedance spectra over the large frequency range and ionic concentrations caused by both the materials and the device complexity cannot be explained by simple models. We report on modeling of OECTs by an optimized equivalent circuit model that takes into account the frequency dependence of the device impedance from 1 Hz to 1 MHz for a large ionic concentration range (10-4 - 1 M) and various chemical nature of the ions. Based on experimental data for KCl(aq) and CaCl2(aq). the model explains the time dependency of the OECT as a whole and discusses the sensibility of new introduced elements pseudo-capacitance and inductance to concentration and voltage to understand the local physics. In particular, the observed concentrat ion-dependent negative phase change in the impedance suggests an inductive contribution to the device impedance due to the doping/dedoping process in the organic layer driven by the applied harmonic voltage as an underlying mechanism . The introduction of these non-redundant elements and the study of their behaviors as function of ionic concentration and applied voltage give a more detailed picture of the OECT working principles at a specific time domains which are highly relevant for multi-parametric ion sensing and neuromorphic computing.
Pecqueur S., Lončarić I., Zlatić V., Vuillaume D., Crljen Ž.
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.
Athanasiou V.*, Pecqueur S., Vuillaume D., Konkoli Z.*
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 CaCl2(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.
Pecqueur S.*, Lončarić I., Zlatić V., Vuillaume D., Crljen Ž.*
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.
Pecqueur S., Guérin D., Vuillaume D., Alibart F.
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.
Pecqueur S., Vuillaume D., Alibart F.*
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.
Pecqueur S., Guérin D., Vuillaume D., Alibart F.
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.
Pecqueur S.*, Mastropasqua Talamo M., Guérin D., Blanchard P., Roncali J., Vuillaume D., Alibart F.*
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.
Pecqueur S.*, Guérin D., Vuillaume D., Alibart F.*
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.
Pecqueur S., Lenfant S., Guérin D., Alibart F., Vuillaume D.
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.
Pecqueur S.*, Lenfant S., Guérin D., Alibart F., Vuillaume D.
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.
Pecqueur S., Lenfant S., Guérin D., Alibart F., Vuillaume D.
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