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Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and readout of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and < 10-nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating-gate memory array based on a polymer redox transistor connected to a conductive-bridge memory (CBM). Selective and linear programming of a redox transistor array is executed in parallel by overcoming the bridging threshold voltage of the CBMs. Synaptic weight readout with currents < 10 nanoamperes is achieved by diluting the conductive polymer with an insulator to decrease the conductance. The redox transistors endure >1 billion write-read operations and support 1-megahertz write-read frequencies.

A neuromorphic device based on the stable electrochemical fine-tuning of the conductivity of an organic ionic/electronic conductor is realized. These devices show high linearity, low noise and extremely low switching voltage. The brain is capable of massively parallel information processing while consuming only ∼1–100 fJ per synaptic event 1 , 2 . Inspired by the efficiency of the brain, CMOS-based neural architectures 3 and memristors 4 , 5 are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy (<10 pJ for 10 3  μm 2 devices), displays >500 distinct, non-volatile conductance states within a ∼1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems 6 , 7 . Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.

We report a strategy for realizing tunable electrical conductivity in metal-organic frameworks (MOFs) in which the nanopores are infiltrated with redox-active, conjugated guest molecules. This approach is demonstrated using thin-film devices of the MOF Cu₃(BTC)₂ (also known as HKUST-1; BTC, benzene-1,3,5-tricarboxylic acid) infiltrated with the molecule 77,8,8-tetracyanoquinododimethane (TCNQ). Tunable, air-stable electrical conductivity over six orders of magnitude is achieved, with values as high as 7 Siemens per meter. Spectroscopic data and first-principles modeling suggest that the conductivity arises from TCNQ guest molecules bridging the binuclear copper paddlewheels in the framework, leading to strong electronic coupling between the dimeric Cu subunits. These ohmically conducting porous MOFs could have applications in conformai electronic devices, reconfigurable electronics, and sensors.

With vibrant colours and simple, room-temperature processing methods, electrochromic polymers have attracted attention as active materials for flexible, low-power-consuming devices. However, slow switching speeds in devices realized to date, as well as the complexity of having to combine several distinct polymers to achieve a full-colour gamut, have limited electrochromic materials to niche applications. Here we achieve fast, high-contrast electrochromic switching by significantly enhancing the interaction of light—propagating as deep-subwavelength-confined surface plasmon polaritons through arrays of metallic nanoslits, with an electrochromic polymer—present as an ultra-thin coating on the slit sidewalls. The switchable configuration retains the short temporal charge-diffusion characteristics of thin electrochromic films, while maintaining the high optical contrast associated with thicker electrochromic coatings. We further demonstrate that by controlling the pitch of the nanoslit arrays, it is possible to achieve a full-colour response with high contrast and fast switching speeds, while relying on just one electrochromic polymer. Slow switching speeds in device configurations have severely limited the applications of electrochromic materials. Here, Xu et al . use plasmonic nanoslit arrays and demonstrate fast, high-contrast, monochromatic and full-colour electrochromic switching using two different electrochromic polymers.

Photoelectrochemical (PEC) water splitting represents a promising route for renewable production of hydrogen, but trade-offs between photoelectrode stability and efficiency have greatly limited the performance of PEC devices. In this work, we employ a metal–insulator–semiconductor (MIS) photoelectrode architecture that allows for stable and efficient water splitting using narrow bandgap semiconductors. Substantial improvement in the performance of Si-based MIS photocathodes is demonstrated through a combination of a high-quality thermal SiO 2 layer and the use of bilayer metal catalysts. Scanning probe techniques were used to simultaneously map the photovoltaic and catalytic properties of the MIS surface and reveal the spillover-assisted evolution of hydrogen off the SiO 2 surface and lateral photovoltage driven minority carrier transport over distances that can exceed 2 cm. The latter finding is explained by the photo- and electrolyte-induced formation of an inversion channel immediately beneath the SiO 2 /Si interface. These findings have important implications for further development of MIS photoelectrodes and offer the possibility of highly efficient PEC water splitting. Photoelectrochemical water-splitting is a promising route for the renewable production of hydrogen, but trade-offs between photoelectrode stability and efficiency remain problematic. A metal–oxide–semiconductor photoelectrode architecture demonstrates stable and efficient water splitting using narrow-bandgap semiconductors. Substantial improvement in the performance of Si-based photocathodes is achieved by combining a high-quality SiO 2 layer and bilayer metal catalysts.

While digital computers rely on software-generated pseudo-random number generators, hardware-based true random number generators (TRNGs), which employ the natural physics of the underlying hardware, provide true stochasticity, and power and area efficiency. Research into TRNGs has extensively relied on the unpredictability in phase transitions, but such phase transitions are difficult to control given their often abrupt and narrow parameter ranges (e.g., occurring in a small temperature window). Here we demonstrate a TRNG based on self-oscillations in LaCoO 3 that is electrically biased within its spin crossover regime. The LaCoO 3 TRNG passes all standard tests of true stochasticity and uses only half the number of components compared to prior TRNGs. Assisted by phase field modeling, we show how spin crossovers are fundamentally better in producing true stochasticity compared to traditional phase transitions. As a validation, by probabilistically solving the NP-hard max-cut problem in a memristor crossbar array using our TRNG as a source of the required stochasticity, we demonstrate solution quality exceeding that using software-generated randomness. Probabilistic computing demands low power and high quality random number generation. Woo et al. demonstrate the use of a spin crossover in LaCoO3 to generate random numbers that outperform software-generated random numbers in probabilistic computing.

Flexible electronic skin with features that include sensing, processing, and responding to stimuli have transformed human–robot interactions. However, more advanced capabilities, such as human‐like self‐protection modalities with a sense of pain, sign of injury, and healing, are more challenging. Herein, a novel, flexible, and robust diffusive memristor based on a copolymer of chlorotrifluoroethylene and vinylidene fluoride (FK‐800) as an artificial nociceptor (pain sensor) is reported. Devices composed of Ag/FK‐800/Pt have outstanding switching endurance >106 cycles, orders of magnitude higher than any other two‐terminal polymer/organic memristors in literature (typically 102–103 cycles). In situ conductive atomic force microscopy is employed to dynamically switch individual filaments, which demonstrates that conductive filaments correlate with polymer grain boundaries and FK‐800 has superior morphological stability under repeated switching cycles. It is hypothesized that the high thermal stability and high elasticity of FK‐800 contribute to the stability under local Joule heating associated with electrical switching. To mimic biological nociceptors, four signature nociceptive characteristics are demonstrated: threshold triggering, no adaptation, relaxation, and sensitization. Lastly, by integrating a triboelectric generator (artificial mechanoreceptor), memristor (artificial nociceptor), and light emitting diode (artificial bruise), the first bioinspired injury response system capable of sensing pain, showing signs of injury, and healing, is demonstrated. A flexible polymeric memristor with cycling endurance matching inorganic memristors is demonstrated to have the key properties of an artificial nociceptor. In‐situ conductive atomic force microscopy is used to dynamically switch individual filaments to provide insights on the switching endurance. The device is integrated into a bioinspired injury response system capable of sensing pain, showing signs of injury, and healing.

In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analog memory is the electrochemical random-access memory (ECRAM), also known as the redox transistor. Its low write currents and linear switching properties across hundreds of analog states enable accurate and massively parallel updates of a full crossbar array, which yield rapid and energy-efficient training. While simulations predict that ECRAM based neural networks achieve high training accuracy at significantly higher energy efficiency than digital implementations, these predictions have not been experimentally achieved. In this work, we train a 3 × 3 array of ECRAM devices that learns to discriminate several elementary logic gates (AND, OR, NAND). We record the evolution of the network’s synaptic weights during parallel in situ (on-line) training, with outer product updates. Due to linear and reproducible device switching characteristics, our crossbar simulations not only accurately simulate the epochs to convergence, but also quantitatively capture the evolution of weights in individual devices. The implementation of the first in situ parallel training together with strong agreement with simulation results provides a significant advance toward developing ECRAM into larger crossbar arrays for artificial neural network accelerators, which could enable orders of magnitude improvements in energy efficiency of deep neural networks.

Motivated by low cost, low toxicity, mechanical flexibility, and conformability over complex shapes, organic semiconductors are currently being actively investigated as thermoelectric (TE) materials to replace the costly, brittle, and non-eco-friendly inorganic TEs for near-ambient-temperature applications. Metal–organic frameworks (MOFs) share many of the attractive features of organic polymers, including solution processability and low thermal conductivity. A potential advantage of MOFs and MOFs with guest molecules (Guest@MOFs) is their synthetic and structural versatility, which allows both the electronic and geometric structure to be tuned through the choice of metal, ligand, and guest molecules. This could solve the long-standing challenge of finding stable, high-TE-performance n-type organic semiconductors, as well as promote high charge mobility via the long-range crystalline order inherent in these materials. In this article, we review recent advances in the synthesis of MOF and Guest@MOF TEs and discuss how the Seebeck coefficient, electrical conductivity, and thermal conductivity could be tuned to further optimize TE performance.

By inserting potassium into a 3D metal–organic framework band delocalization occurs, enabling mobilities and conductivities similar to organic polymers.