Memristor in a Reservoir System-Experimental Evidence for High-Level Computing and Neuromorphic Behavior of PbI2

Insights into synaptic functionality and resistive switching in lead iodide flexible memristor devices

Neuromorphic platforms are gaining popularity due to their superior efficiency, low power consumption, and adaptable parallel signal processing capabilities, overcoming the limitations of traditional von Neumann architecture. We conduct an in-depth investigation into the factors influencing the resistive switching mechanism in memristor devices utilizing lead iodide (PbI2). We establish correlations between device performance and morphological features, unveiling synaptic like behaviour of device making it suitable for range of flexible neuromorphic applications. Notably, a highly reliable unipolar switching mechanism is identified, exhibiting stability even under mechanical strain (with a bending radius of approximately 4 mm) and in high humidity environment (at 75% relative humidity) without the need for encapsulation. The investigation delves into the complex interplay of charge transport, ion migration and the active interface, elucidating the factors contributing to the remarkable resistive switching observed in PbI2-based memristors. The detailed findings highlight synaptic behaviors akin to the modulation of synaptic strengths, with an impressive potentiation and depression of 2 × 104 cycles, emphasizing the role of spike time-dependent plasticity (STDP). The flexible platform demonstrates exceptional performance, achieving a simulated accuracy rate of 95.06% in recognizing modified patterns from the National Institute of Standards and Technology (MNIST) dataset with just 30 training epochs. Ultimately, this research underscores the potential of PbI2-based flexible memristor devices as versatile component for neuromorphic computing. Moreover, it demonstrate the robustness of PbI2 memristors in terms of their resistive switching capabilities, showcasing resilience both mechanically and electrically. This underscores their potential in replicating synaptic functions for advanced information processing systems.

Towards Embedded Computation with Building Materials

We present results showing the capability of concrete-based information processing substrate in the signal classification task in accordance with in materio computing paradigm. As the Reservoir Computing is a suitable model for describing embedded in materio computation, we propose that this type of presented basic construction unit can be used as a source for “reservoir of states” necessary for simple tuning of the readout layer. We present an electrical characterization of the set of samples with different additive concentrations followed by a dynamical analysis of selected specimens showing fingerprints of memfractive properties. As part of dynamic analysis, several fractal dimensions and entropy parameters for the output signal were analyzed to explore the richness of the reservoir configuration space. In addition, to investigate the chaotic nature and self-affinity of the signal, Lyapunov exponents and Detrended Fluctuation Analysis exponents were calculated. Moreover, on the basis of obtained parameters, classification of the signal waveform shapes can be performed in scenarios explicitly tuned for a given device terminal.

Artificial Tactile Perceptual Neuron with Nociceptive and Pressure Decoding Abilities

The neural system is a multifunctional perceptual learning system. Our brain can perceive different kinds of information to form senses, including touch, sight, hearing, and so on. Mimicking such perceptual learning systems is critical for neuromorphic platform applications. Here, an artificial tactile perceptual neuron is realized by utilizing electronic skins (E-skin) with oxide neuromorphic transistors, and this artificial tactile perceptual neuron successfully simulates biological tactile afferent nerves. First, the E-skin device is constructed using microstructured polydimethylsiloxane membranes coated with Ag/indium tin oxide (ITO) layers, exhibiting good sensitivities of ∼2.1 kPa^-1 and fast response time of tens of milliseconds. Then, the chitosan-based electrolyte-gated ITO neuromorphic transistor is fabricated and exhibits high performance and synaptic responses. Finally, the integrated artificial tactile perceptual neuron demonstrates pressure excitatory postsynaptic current and paired-pulse facilitation. The artificial tactile perceptual neuron is featured with low energy consumption as low as ∼0.7 nJ. Moreover, it can mimic acute and chronic pain and nociceptive characteristics of allodynia and hyperalgesia in biological nociceptors. Interestingly, the artificial tactile perceptual neuron can employ "Morse code" pressure-interpreting scheme. This simple and low-cost approach has excellent potential for applications including but not limited to intelligent humanoid robots and replacement neuroprosthetics.

Modulation of Binary Neuroplasticity in a Heterojunction-Based Ambipolar Transistor

To keep pace with the upcoming big-data era, the development of a device-level neuromorphic system with highly efficient computing paradigms is underway with numerous attempts. Synaptic transistors based on an all-solution processing method have received growing interest as building blocks for neuromorphic computing based on spikes. Here, we propose and experimentally demonstrated the dual operation mode in poly{2,2-(2,5-bis(2-octyldodecyl)-3,6-dioxo-2,3,5,6-tetrahydropyrrolo[3,4-c]pyrrole-1,4-diyl)dithieno[3,2-b]thiophene-5,5-diyl-alt-thiophen-2,5-diyl}(PDPPBTT)/ZnO junction-based synaptic transistor from ambipolar charge-trapping mechanism to analog the spiking interfere with synaptic plasticity. The heterojunction formed by PDPPBTT and ZnO layers serves as the basis for hole-enhancement and electron-enhancement modes of the synaptic transistor. Distinctive synaptic responses of paired-pulse facilitation (PPF) and paired-pulse depression (PPD) were configured to achieve the training/recognition function for digit image patterns at the device-to-system level. The experimental results indicate the potential application of the ambipolar transistor in future neuromorphic intelligent systems.