Perspective: Organic electronic materials and devices for neuromorphic engineering

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 (ICT). In fact, new computing solutions and new hardware platforms are expected 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 pro and cons. In this perspective paper belonging to the special issue on neuromorphic 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 organic materials can be used to engineer useful and promising bioinspired devices and circuits. We also discuss about the opportunities that organic electronic are offering for future research directions in the neuromorphic engineering field

Filamentary Switching: Synaptic Plasticity through Device Volatility

Replicating the computational functionalities and performances of the brain remains one of the biggest challenges for the future of information and communication technologies. Such an ambitious goal requires research efforts from the architecture level to the basic device level (i.e., investigating the opportunities offered by emerging nanotechnologies to build such systems). Nanodevices, or, more precisely, memory or memristive devices, have been proposed for the implementation of synaptic functions, offering the required features and integration in a single component. In this paper, we demonstrate that the basic physics involved in the filamentary switching of electrochemical metallization cells can reproduce important biological synaptic functions that are key mechanisms for information processing and storage. The transition from short- to long-term plasticity has been reported as a direct consequence of filament growth (i.e., increased conductance) in filamentary memory devices. In this paper, we show that a more complex filament shape, such as dendritic paths of variable density and width, can permit the short- and long-term processes to be controlled independently. Our solid-state device is strongly analogous to biological synapses, as indicated by the interpretation of the results from the framework of a phenomenological model developed for biological synapses. We describe a single memristive element containing a rich panel of features, which will be of benefit to future neuromorphic hardware systems

Cation Discrimination in Organic Electrochemical Transistors by Dual Frequency Sensing

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 different 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.Comment: Full text and supporting informatio

An artificial spiking synapse made of molecules and nanoparticles

Molecule-based devices are envisioned to complement silicon devices by providing new functions or already existing functions at a simpler process level and at a lower cost by virtue of their self-organization capabilities, moreover, they are not bound to von Neuman architecture and this may open the way to other architectural paradigms. Here we demonstrate a device made of conjugated molecules and metal nanoparticles (NPs) which behaves as a spiking synapse suitable for integration in neural network architectures. We demonstrate that this device exhibits the main behavior of a biological synapse. These results open the way to rate coding utilization of the NOMFET in perceptron and Hopfield networks. We can also envision the NOMFET as a building block of neuroelectronics for interfacing neurons or neuronal logic devices made from patterned neuronal cultures with solid-state devices and circuits

Expanding memory in recurrent spiking networks

Recurrent spiking neural networks (RSNNs) are notoriously difficult to train because of the vanishing gradient problem that is enhanced by the binary nature of the spikes. In this paper, we review the ability of the current state-of-the-art RSNNs to solve long-term memory tasks, and show that they have strong constraints both in performance, and for their implementation on hardware analog neuromorphic processors. We present a novel spiking neural network that circumvents these limitations. Our biologically inspired neural network uses synaptic delays, branching factor regularization and a novel surrogate derivative for the spiking function. The proposed network proves to be more successful in using the recurrent connections on memory tasks

Conductive filament evolution dynamics revealed by cryogenic (1.5 K) multilevel switching of CMOS-compatible Al2O3/TiO2 resistive memories

This study demonstrates multilevel switching at 1.5 K of Al2O3/TiO2-x resistive memory devices fabricated with CMOS-compatible processes and materials. The I-V characteristics exhibit a negative differential resistance (NDR) effect due to a Joule-heating-induced metal-insulator transition of the Ti4O7 conductive filament. Carrier transport analysis of all multilevel switching I-V curves show that while the insulating regime follows the space charge limited current (SCLC) model for all resistance states, the conduction in the metallic regime is dominated by SCLC and trap-assisted tunneling (TAT) for low- and high-resistance states respectively. A non-monotonic conductance evolution is observed in the insulating regime, as opposed to the continuous and gradual conductance increase and decrease obtained in the metallic regime during the multilevel SET and RESET operations. Cryogenic transport analysis coupled to an analytical model accounting for the metal-insulator-transition-induced NDR effects and the resistance states of the device provide new insights on the conductive filament evolution dynamics and resistive switching mechanisms. Our findings suggest that the non-monotonic conductance evolution in the insulating regime is due to the combined effects of longitudinal and radial variations of the Ti4O7 conductive filament during the switching. This behavior results from the interplay between temperature- and field-dependent geometrical and physical characteristics of the filament.Comment: 8 pages, 4 figure

Observation of Highly Nonlinear Resistive Switching of Al2O3/TiO2-x Memristors at Cryogenic Temperature (1.5 K)

In this work, we investigate the behavior of Al2O3/TiO2-x cross-point memristors in cryogenic environment. We report successful resistive switching of memristor devices from 300 K down to 1.5 K. The I-V curves exhibit negative differential resistance effects between 130 and 1.5 K, attributed to a metal-insulator transition (MIT) of the Ti4O7 conductive filament. The resulting highly nonlinear behavior is associated to a maximum ION/IOFF ratio of 84 at 1.5 K, paving the way to selector-free cryogenic passive crossbars. Finally, temperature-dependant thermal activation energies related to the conductance at low bias (20 mV) are extracted for memristors in low resistance state, suggesting hopping-type conduction mechanisms.Comment: 4 pages, 4 figures, IEEE 14th Nanotechnology Materials & Devices Conference (NMDC 2019

Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition

Surface electromyogram (sEMG) signals result from muscle movement and hence they are an ideal candidate for benchmarking event-driven sensing and computing. We propose a simple yet novel approach for optimizing the spike encoding algorithm's hyper-parameters inspired by the readout layer concept in reservoir computing. Using a simple machine learning algorithm after spike encoding, we report performance higher than the state-of-the-art spiking neural networks on two open-source datasets for hand gesture recognition. The spike encoded data is processed through a spiking reservoir with a biologically inspired topology and neuron model. When trained with the unsupervised activity regulation CRITICAL algorithm to operate at the edge of chaos, the reservoir yields better performance than state-of-the-art convolutional neural networks. The reservoir performance with regulated activity was found to be 89.72% for the Roshambo EMG dataset and 70.6% for the EMG subset of sensor fusion dataset. Therefore, the biologically-inspired computing paradigm, which is known for being power efficient, also proves to have a great potential when compared with conventional AI algorithms.Comment: Accepted to International Conference on Neuromorphic Systems (ICONS 2021

Low voltage and time constant organic synapse-transistor

We report on an artificial synapse, an organic synapse-transistor (synapstor) working at 1 volt and with a typical response time in the range 100-200 ms. This device (also called NOMFET, Nanoparticle Organic Memory Field Effect Transistor) combines a memory and a transistor effect in a single device. We demonstrate that short-term plasticity (STP), a typical synaptic behavior, is observed when stimulating the device with input spikes of 1 volt. Both significant facilitating and depressing behaviors of this artificial synapse are observed with a relative amplitude of about 50% and a dynamic response < 200 ms. From a series of in-situ experiments, i.e. measuring the current-voltage characteristic curves in-situ and in real time, during the growth of the pentacene over a network of gold nanoparticles, we elucidate these results by analyzing the relationship between the organic film morphology and the transport properties. This synapstor works at a low energy of about 2 nJ/spike. We discuss the implications of these results for the development of neuro-inspired computing architectures and interfacing with biological neurons.Comment: Full paper with supporting informatio