The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near‐memory computing, help alleviate the data communication bottleneck to some extent, but paradigm‐shifting concepts are required. In‐memory computing has emerged as a prime candidate to eliminate this bottleneck by colocating memory and processing. In this context, resistive switching (RS) memory devices is a key promising choice, due to their unique intrinsic device‐level properties, enabling both storing and computing with a small, massively‐parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. A qualitative and quantitative analysis of several key existing challenges in implementing high‐capacity, high‐volume RS memories for accelerating the most computationally demanding computation in machine learning (ML) inference, that of vector‐matrix multiplication (VMM), is presented. The monolithic integration of RS memories with complementary metal–oxide–semiconductor (CMOS) integrated circuits is presented as the core underlying technology. The key existing design choices in terms of device‐level physical implementation, circuit‐level design, and system‐level considerations is reviewed and an outlook for future directions is provided.

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 realized 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, and structural plasticity, which are essential features required for future neuromorphic hardware development.

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

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 algorithms 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 the 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 computing 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.

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 investigation 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 iono‐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 approach 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 interest in bio‐signals sorting applications.