High-performance one-dimensional halide perovskite crossbar memristors and synapses for neuromorphic computing.

Despite impressive demonstrations of memristive behavior with halide perovskites, no clear pathway for material and device design exists for their applications in neuromorphic computing. Present approaches are limited to single element structures, fall behind in terms of switching reliability and scalability, and fail to map out the analog programming window of such devices. Here, we systematically design and evaluate robust pyridinium-templated one-dimensional halide perovskites as crossbar memristive materials for artificial neural networks. We compare two halide perovskite 1D inorganic lattices, namely (propyl)pyridinium and (benzyl)pyridinium lead iodide. The absence of conjugated, electron-rich substituents in PrPyr+ prevents edge-to-face type π-stacking, leading to enhanced electronic isolation of the 1D iodoplumbate chains in (PrPyr)[PbI3], and hence, superior resistive switching performance compared to (BnzPyr)[PbI3]. We report outstanding resistive switching behaviours in (PrPyr)[PbI3] on the largest flexible crossbar implementation (16 × 16) to date - on/off ratio (>105), long term retention (105 s) and high endurance (2000 cycles). Finally, we put forth a universal approach to comprehensively map the analog programming window of halide perovskite memristive devices - a critical prerequisite for weighted synaptic connections in artificial neural networks. This consequently facilitates the demonstration of accurate handwritten digit recognition from the MNIST database based on spike-timing-dependent plasticity of halide perovskite memristive synapses.

Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics.

Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decision-making, 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.

High-k, Ultrastretchable Self-Enclosed Ionic Liquid-Elastomer Composites for Soft Robotics and Flexible Electronics.

Soft robotics focuses on mimicking natural systems to produce dexterous motion. Dielectric elastomer actuators (DEAs) are an attractive option due to their large strains, high efficiencies, lightweight design, and integrability, but require high electric fields. Conventional approaches to improve DEA performance by incorporating solid fillers in the polymer matrices can increase the dielectric constant but to the detriment of mechanical properties. In the present work, we draw inspiration from soft and deformable human skin, enabled by its unique structure, which consists of a fluid-filled membrane, to create self-enclosed liquid filler (SELF)-polymer composites by mixing an ionic liquid into the elastomeric matrix. Unlike hydrogels and ionogels, the SELF-polymer composites are made from immiscible liquid fillers, selected based on interfacial interaction with the elastomer matrix, and exist as dispersed globular phases. This combination of structure and filler selection unlocks synergetic improvements in electromechanical properties-doubling of dielectric constant, 100 times decrease in Young's modulus, and ∼5 times increase in stretchability. These composites show superior thermal stability to volatile losses, combined with excellent transparency. These ultrasoft high-k composites enable a significant improvement in the actuation performance of DEAs-longitudinal strain (5 times) and areal strain (8 times)-at low applied nominal electric fields (4 V/µm). They also enable high-sensitivity capacitive pressure sensors without the need of miniaturization and microstructuring. This class of self-enclosed ionic liquid polymer composites could impact the areas of soft robotics, shape morphing, flexible electronics, and optoelectronics.

Directed Assembly of Liquid Metal-Elastomer Conductors for Stretchable and Self-Healing Electronics.

Growing interest in soft robotics, stretchable electronics, and electronic skins has created demand for soft, compliant, and stretchable electrodes and interconnects. Here, dielectrophoresis (DEP) is used to assemble, align, and sinter eutectic gallium indium (EGaIn) microdroplets in uncured poly(dimethylsiloxane) (PDMS) to form electrically conducting microwires. There are several noteworthy aspects of this approach. 1) Generally, EGaIn droplets in silicone at loadings approaching 90 wt% remain insulating and form a conductive network only when subjected to sintering. Here, DEP facilitates assembly of EGaIn droplets into conductive microwires at loadings as low as 10 wt%. 2) DEP is done in silicone for the first time, enabling the microwires to be cured in a stretchable matrix. 3) Liquid EGaIn droplets sinter during DEP to form a stretchable metallic microwire that retains its shape after curing the silicone. 4) Use of liquid metal eliminates the issue of compliance mismatch observed in soft polymers with solid fillers. 5) The silicone-EGaIn "ink" can be assembled by DEP within the crevices of severely damaged wires to create stretchable interconnects that heal the damage mechanically and electrically. The DEP process of this unique set of materials is characterized and the interesting attributes enabled by such liquid microwires are demonstrated.

Field-Driven Athermal Activation of Amorphous Metal Oxide Semiconductors for Flexible Programmable Logic Circuits and Neuromorphic Electronics.

Despite extensive research, large-scale realization of metal-oxide electronics is still impeded by high-temperature fabrication, incompatible with flexible substrates. Ideally, an athermal treatment modifying the electronic structure of amorphous metal oxide semiconductors (AMOS) to generate sufficient carrier concentration would help mitigate such high-temperature requirements, enabling realization of high-performance electronics on flexible substrates. Here, a novel field-driven athermal activation of AMOS channels is demonstrated via an electrolyte-gating approach. Facilitating migration of charged oxygen species across the semiconductor-dielectric interface, this approach modulates the local electronic structure of the channel, generating sufficient carriers for charge transport and activating oxygen-compensated thin films. The thin-film transistors (TFTs) investigated here depict an enhancement of linear mobility from 51 to 105.25 cm2 V-1 s-1 (ionic-gated) and from 8.09 to 14.49 cm2 V-1 s-1 (back-gated), by creating additional oxygen vacancies. The accompanying stochiometric transformations, monitored via spectroscopic measurements (X-ray photoelectron spectroscopy) corroborate the detailed electrical (TFT, current evolution) parameter analyses, providing critical insights into the underlying oxygen-vacancy generation mechanism and clearly demonstrating field-induced activation as a promising alternative to conventional high-temperature annealing strategies. Facilitating on-demand active programing of the operation modes of transistors (enhancement vs depletion), this technique paves way for facile fabrication of logic circuits and neuromorphic transistors for bioinspired computing.

Ultralow Power Dual-Gated Subthreshold Oxide Neuristors: An Enabler for Higher Order Neuronal Temporal Correlations.

Inspired by neural computing, the pursuit of ultralow power neuromorphic architectures with highly distributed memory and parallel processing capability has recently gained more traction. However, emulation of biological signal processing via artificial neuromorphic architectures does not exploit the immense interplay between local activities and global neuromodulations observed in biological neural networks and hence are unable to mimic complex biologically plausible adaptive functions like heterosynaptic plasticity and homeostasis. Here, we demonstrate emulation of complex neuronal behaviors like heterosynaptic plasticity, homeostasis, association, correlation, and coincidence in a single neuristor via a dual-gated architecture. This multiple gating approach allows one gate to capture the effect of local activity correlations and the second gate to represent global neuromodulations, allowing additional modulations which augment their plasticity, enabling higher order temporal correlations at a unitary level. Moreover, the dual-gate operation extends the available dynamic range of synaptic conductance while maintaining symmetry in the weight-update operation, expanding the number of accessible memory states. Finally, operating neuristors in the subthreshold regime enable synaptic weight changes with high gain while maintaining ultralow power consumption of the order of femto-Joules.

Ionotronic Halide Perovskite Drift-Diffusive Synapses for Low-Power Neuromorphic Computation.

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 long-term 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.

Indium Tungsten Oxide Thin Films for Flexible High-Performance Transistors and Neuromorphic Electronics.

Thin-film transistors (TFTs) with high electrical performances (mobility > 10 cm2/V s, Vth < 1 V, SS < 1 V/decade, on/off ratio ≈ 106) obtained from the silicon- and oxide-based single-crystalline semiconductor materials require high processing temperature and hence are not suitable for flexible electronics. Amorphous oxide-based transparent electronic devices are attractive to meet emerging technological demands where crystalline oxide-/silicon-based architectures cannot provide a solution. Here, we tackle this problem by using a novel amorphous oxide semiconducting material-namely, indium tungsten oxide (IWO)-as the active channel in flexible TFTs (FTFTs). Post-annealing temperature as low as 270 °C for amorphous IWO thin films deposited by radio frequency sputtering at room temperature could result in smooth morphology ( Rrms ≈ 0.42 nm), good adhesion, and high carrier density ( n ≈ 7.19 × 1018 cm-3). Excellent TFT characteristics of flexible devices could be achieved with linear field effect mobility µFE ≈ 25.86 cm2/V s, subthreshold swing SS ≈ 0.30 V/decade, threshold voltage Vth ≈ -1.5 V, and on/off ratio Ion/ Ioff ≈ 5.6 × 105 at 3 V and stable operation during bending of the FTFT. Additionally, IWO TFTs were implemented as synapses, the building block for neuromorphic computing. Paired-pulse facilitation up to 138% was observed and showed an exponential decay resembling chemical synapses. Utilizing this characteristic, a high-pass dynamic temporal filter was devised providing increased gain from 1.55 to 21 when frequency was raised from 22 to 62 Hz. The high performance and stability of flexible TFTs obtained with IWO films demonstrate their promise for low-voltage electronic applications.

Synergistic Gating of Electro-Iono-Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity.

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 (MoS2 ) 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.

Healable and flexible transparent heaters.

Challenges associated with the mechanical fracture of electrical conductors have hindered the realization of truly flexible high performance wearable electronics. Here, transparent healable electrodes have been developed and examined to alleviate these problems. The composite electrode features a layer of an interconnecting AgNW network on a polyurethane film modified with Diels-Alder adducts (PU-DA). Surface modification using hydrophilic molecules improved adhesion of the AgNW network and resulted in mechanically robust flexible electrodes with a figure of merit sheet resistance of 13.3 Ω â–¡-1 and 77% transmittance at 550 nm. Transparent and flexible healable heaters (TFHH) with good mechanical and thermal stability were fabricated using these electrodes for potential applications in thermochromics, electrically driven displays and defrosters. The PU-DA TFHHs exhibited high Joule heating temperatures of 102 °C with a low operation voltage (6 V), fast thermal response (150 s) and enhanced robustness to endure large repeated mechanical strain for over 500 bending cycles with a small variance in resistance (<10%). After deliberate damage by a knife cut, the electrodes were healed and recovered to their original conductivity via a simple heat treatment at 120 °C. Uniquely, the healing process can also be triggered by utilising electrical power.

Flexible Ionic-Electronic Hybrid Oxide Synaptic TFTs with Programmable Dynamic Plasticity for Brain-Inspired Neuromorphic Computing.

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 cm2 V-1 s-1 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.

Transparent Flexible Multifunctional Nanostructured Architectures for Non-optical Readout, Proximity, and Pressure Sensing.

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.