Analog resistive memories promise to reduce the energy of neural networks by orders of magnitude. However, the write variability and write nonlinearity of current devices prevent neural networks from training to high accuracy. We present a novel periodic carry method that uses a positional number system to overcome this while maintaining the benefit of parallel analog matrix operations. We demonstrate how noisy, nonlinear TaOx devices that could only train to 80% accuracy on MNIST, can now reach 97% accuracy, only 1% away from an ideal numeric accuracy of 98%. On a file type dataset, the TaOx devices achieve ideal numeric accuracy. In addition, low noise, linear Li1-xCoO2 devices train to ideal numeric accuracies using periodic carry on both datasets.
Several active areas of research in novel energy storage technologies, including three-dimensional solid state batteries and passivation coatings for reactive battery electrode components, require conformal solid state electrolytes. We describe an atypical atomic layer deposition (ALD) process for a member of the lithium phosphorus oxynitride (LiPON) family, which is employed as a thin film lithium-conducting solid electrolyte. The reaction between lithium tert-butoxide (LiOtBu) and diethyl phosphoramidate (DEPA) produces conformal, ionically conductive thin films with a stoichiometry close to Li2PO2N between 250 and 300 °C. Unusually, the P/N ratio of the films is always 1, indicative of a particular polymorph of LiPON that closely resembles a polyphosphazene. Films grown at 300 °C have an ionic conductivity of (6.51 ± 0.36) × 10-7 S/cm at 35 °C and are functionally electrochemically stable in the window from 0 to 5.3 V versus Li/Li+. We demonstrate the viability of the ALD-grown electrolyte by integrating it into full solid state batteries, including thin film devices using LiCoO2 as the cathode and Si as the anode operating at up to 1 mA/cm2. The high quality of the ALD growth process allows pinhole-free deposition even on rough crystalline surfaces, and we demonstrate the successful fabrication and operation of thin film batteries with ultrathin (<100 nm) solid state electrolytes. Finally, we show an additional application of the moderate-temperature ALD process by demonstrating a flexible solid state battery fabricated on a polymer substrate.
The brain is capable of massively parallel information processing while consuming only ~1-100 fJ per synaptic event1,2. Inspired by the efficiency of the brain, CMOS-based neural architectures3 and memristors4,5 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. ENODeswitches at lowvoltage and energy (<10 pJ for 103 μm2 devices), displays >500 distinct, non-volatile conductance states within a~1V 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 systems6,7. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.