Emulation of brain-like signal processing with thin-film devices can lay the foundation for building artificially intelligent learning circuitry in future. Encompassing higher functionalities into single artificial neural elements will allow the development of robust neuromorphic circuitry emulating biological adaptation mechanisms with drastically lesser neural elements, mitigating strict process challenges and high circuit density requirements necessary to match the computational complexity of the human brain. Here, 2D transition metal di-chalcogenide (MoS ) neuristors are designed to mimic intracellular ion endocytosis-exocytosis dynamics/neurotransmitter-release in chemical synapses using three approaches: (i) electronic-mode: a defect modulation approach where the traps at the semiconductor-dielectric interface are perturbed; (ii) ionotronic-mode: where electronic responses are modulated via ionic gating; and (iii) photoactive-mode: harnessing persistent photoconductivity or trap-assisted slow recombination mechanisms. Exploiting a novel multigated architecture incorporating electrical and optical biases, this incarnation not only addresses different charge-trapping probabilities to finely modulate the synaptic weights, but also amalgamates neuromodulation schemes to achieve "plasticity of plasticity-metaplasticity" via dynamic control of Hebbian spike-time dependent plasticity and homeostatic regulation. Coexistence of such multiple forms of synaptic plasticity increases the efficacy of memory storage and processing capacity of artificial neuristors, enabling design of highly efficient novel neural architectures.

additional analog converters, imposing issues with scalability and power consumption. [2][3][4][5] Development of next-generation materials and devices for neuromorphic electronics entails detailed understanding of the fundamental device characteristics and their possible emulation capabilities at an elemental level. Ionically gated transistors harness diffusive mechanics to achieve continuous modulation of channel conductance at low-power, but require coupling of two disparate electronically and ionically active material sets. [6,7] Solutions based on drift-memristors are inherently disadvantaged due to digital-like abrupt switching transitions, which limit their plasticity. [8] Very recently, second-order drift memristors, [9,10] electrochemical metallization cells, [11] and diffusive memristors [8] have been engineered to approximate the biological Ca 2+ dynamics based on metal atom diffusion, thermal dissipation, [9] mobility decay, [12] and spontaneous nanoparticle formation, but often require additional nonvolatile elements in series for long-term memory storage. An ionic semiconductor which intimately combines rapid electronic transitions with slow drift-diffusive ionic kinetics will enable dynamic tuning of metastable memristive conductance states, allowing efficient emulation of synaptic characteristics and catering for novel low-power architectures that exploit electronic properties of the semiconductor.Emulation of brain-like signal processing is the foundation for development of efficient learning circuitry, but few devices offer the tunable conductance range necessary for mimicking spatiotemporal plasticity in biological synapses. An ionic semiconductor which couples electronic transitions with drift-diffusive ionic kinetics would enable energy-efficient analog-like switching of metastable conductance states. Here, ionic-electronic coupling in halide perovskite semiconductors is utilized to create memristive synapses with a dynamic continuous transition of conductance states. Coexistence of carrier injection barriers and ion migration in the perovskite films defines the degree of synaptic plasticity, more notable for the larger organic ammonium and formamidinium cations than the inorganic cesium counterpart. Optimized pulsing schemes facilitates a balanced interplay of short-and longterm plasticity rules like paired-pulse facilitation and spike-time-dependent plasticity, cardinal for learning and computing. Trained as a memory array, halide perovskite synapses demonstrate reconfigurability, learning, forgetting, and fault tolerance analogous to the human brain. Network-level simulations of unsupervised learning of handwritten digit images utilizing experimentally derived device parameters, validates the utility of these memristors for energy-efficient neuromorphic computation, paving way for novel ionotronic neuromorphic architectures with halide perovskites as the active material. Artificial SynapsesThe ORCID identification number(s) for the author(s) of this article can be found under https://doi.C...

Emulation of biological synapses is necessary for future brain-inspired neuromorphic computational systems that could look beyond the standard von Neuman architecture. Here, artificial synapses based on ionic-electronic hybrid oxide-based transistors on rigid and flexible substrates are demonstrated. The flexible transistors reported here depict a high field-effect mobility of ≈9 cm V s with good mechanical performance. Comprehensive learning abilities/synaptic rules like paired-pulse facilitation, excitatory and inhibitory postsynaptic currents, spike-time-dependent plasticity, consolidation, superlinear amplification, and dynamic logic are successfully established depicting concurrent processing and memory functionalities with spatiotemporal correlation. The results present a fully solution processable approach to fabricate artificial synapses for next-generation transparent neural circuits.

With the current research impetus on neuromorphic computing hardware, realizing efficient drift and diffusive memristors are considered critical milestones for the implementation of readout layers, selectors, and frameworks in deep learning and reservoir computing networks. Current demonstrations are predominantly limited to oxide insulators with a soft breakdown behavior. While organic ionotronic electrochemical materials offer an attractive alternative, their implementations thus far have been limited to features exploiting ionic drift a.k.a. drift memristor technology. Development of diffusive memristors with organic electrochemical materials is still at an early stage, and modulation of their switching dynamics remains unexplored. Here, halide perovskite (HP) memristive barristors (diodes with variable Schottky barriers) portraying tunable diffusive dynamics and ionic drift are proposed and experimentally demonstrated. An ion permissive poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate interface that promotes diffusive kinetics and an ion source nickel oxide (NiOx) interface that supports drift kinetics are identified to design diffusive and drift memristors, respectively, with methylammonuim lead bromide (CH3NH3PbBr3) as the switching matrix. In line with the recent interest on developing artificial afferent nerves as information channels bridging sensors and artificial neural networks, these HP memristive barristors are fashioned as nociceptive and synaptic emulators for neuromorphic sensory signal computing.

Ultralow power dual-gated subthreshold oxide neuristors : an enabler for higher order neuronal temporal correlations

Electronic skins need to be versatile and able to detect multiple inputs beyond simple pressure and touch while having attributes of transparency and facile manufacturability. Herein, we demonstrate a versatile nanostructured transparent sensor capable of detecting wide range of pressures and proximity as well as novel nonoptical detection of printed patterns. The architecture and fabrication processes are straightforward and show robustness to repeated cycling and testing. The sensor displays good sensitivity and stability from 30 Pa to 5 kPa without the use of microstructuration and is conformal and sensitive to be utilized as a wrist-based heart-rate monitor. Highly sensitive proximity detection is shown from a distance of 9 cm. Finally, a unique nonoptical pattern recognition dependent on the difference in the dielectric constant between ink and paper is also demonstrated, indicating the multifunctionality of this simple architecture.

Due to the growing interest in soft robotics, stretchable electronics, and electronic skins, there is demand for compliant electrodes and interconnects that are soft, stretchable and conductive.Here, we use dielectrophoresis (DEP) to assemble, align, and sinter microdroplets of liquid metal-eutectic Ga-In (EGaIn)-in uncured polydimethylsiloxane (PDMS) to form electrically conducting microwires. There are several noteworthy aspects of this approach: (1) Generally, liquid metal droplets in silicone at loadings approaching 90 wt% remain insulating and form conductive network only when subjected to sintering. Here the use of DEP facilitates assembly of the filler droplets into conductive microwires at loadings as low as 10 wt% EGaIn. (2) Dielectrophoresis is done in silicone for the first time, enabling the microwires to be cured in a stretchable matrix. (3) Because the droplets are liquid, they sinter during dielectrophoresis to form a stretchable metallic microwire that retains its shape after curing the silicone and does not change resistance during mechanical strain. (4) The use of liquid metal eliminates the issue of compliance mismatch observed in soft polymers with solid fillers. (5) The silicone-EGaIn "ink" can be placed in holes created by severely damaged regions of stretchable wires to create stretchable interconnects that heal the damage both mechanically and electrically. We characterize the DEP process of this unique set of materials and demonstrate the interesting attributes enabled by such liquid microwires.

Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decisionmaking, requiring the transfer of large amounts of data from periphery to central processors, at the cost of wiring, latency, fault tolerance and robustness. We envision a decentralized approach where intelligence is embedded in the sensing nodes, using a unique neuromorphic methodology to extract relevant information in robotic skins. Here we specifically address pain perception and the association of nociception with tactile perception to trigger the escape reflex in a sensorized robotic arm. The proposed system comprises self-healable materials and memtransistors as enabling technologies for the implementation of neuromorphic nociceptors, spiking local associative learning and communication. Configuring memtransistors as gated-threshold and-memristive switches, the demonstrated system features in-memory edge computing with minimal hardware circuitry and wiring, and enhanced fault tolerance and robustness.