Detailed understanding of solid–solid interface structure–function relationships is critical for the improvement and wide deployment of all-solid-state batteries. The interfaces between lithium phosphorous oxynitride (LiPON) solid electrolyte material and lithium metal anode, and between LiPON and LixCoO2 cathode, have been reported to generate solid–electrolyte interphase (SEI)-like products and/or disordered regions. Using electronic structure calculations and crystalline LiPON models, we predict that LiPON models with purely P−N−P backbones are kinetically inert towards lithium at room temperature. In contrast, transfer of oxygen atoms from low-energy LixCoO2(104) surfaces to LiPON is much faster under ambient conditions. The mechanisms of the primary reaction steps, LiPON structural motifs that readily reacts with lithium metal, experimental results on amorphous LiPON to partially corroborate these predictions, and possible mitigation strategies to reduce degradations are discussed. LiPON interfaces are found to be useful case studies for highlighting the importance of kinetics-controlled processes during battery assembly at moderate processing temperatures.
Neuromorphic devices are becoming increasingly appealing as efficient emulators of neural networks used to model real world problems. However, no hardware to date has demonstrated the necessary high accuracy and energy efficiency gain over CMOS in both (1) training via backpropagation and (2) in read via vector matrix multiplication. Such shortcomings are due to device non-idealities, particularly asymmetric conductance tuning in response to uniform voltage pulse inputs. Here, by formulating a general circuit model for capacitive ion-exchange neuromorphic devices, we show that asymmetric nonlinearity in organic electrochemical neuromorphic devices (ENODes) can be suppressed by an appropriately chosen write scheme. Simulations based upon our model suggest that a nonlinear write-selector could reduce the switching voltage and energy, enabling analog tuning via a continuous set of resistance states (100 states) with extremely low switching energy (∼170 fJ • μm-2). This work clarifies the pathway to neural algorithm accelerators capable of parallelism during both read and write operations.
Major advances in thin-film solid-state batteries (TFSSBs) may capitalize on 3D structuring using high-aspect-ratio substrates such as nanoscale pits, pores, trenches, flexible polymers, and textiles. This will require conformal processes such as atomic layer deposition (ALD) for every active functional component of the battery. Here we explore the deposition and electrochemical properties of SnO2, SnNy, and SnOxNy thin films as TFSSB anode materials, grown by ALD using tetrakisdimethylamido(tin), H2O, and N2 plasma as precursors. By controlling the dose ratio between H2O and N2, the N-O fraction can be tuned between 0% N and 95% N. The electrochemical properties of these materials were tested across a composition range varying from pure SnO2, to SnON intermediates, and pure SnNy. In TFSSBs, the SnNy anodes are found to be more stable during cycling than the SnO2 or SnOxNy films, with an initial reversible capacity beyond that of Li-Sn alloying, retaining 75% of their capacity over 200 cycles compared to only 50% for SnO2. Furthermore, the performance of the SnOxNy anodes indicates that SnNy anodes should not be negatively impacted by small levels of O contamination.
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.
The thermal conductivity of n- and p-type doped gallium nitride (GaN) epilayers having thicknesses of 3-4 μm was investigated using time domain thermoreflectance. Despite possessing carrier concentrations ranging across 3 decades (1015-1018cm-3), n-type layers exhibit a nearly constant thermal conductivity of 180 W/mK. The thermal conductivity of p-type epilayers, in contrast, reduces from 160 to 110 W/mK with increased doping. These trends - and their overall reduction relative to bulk - are explained leveraging established scattering models where it is shown that, while the decrease in p-type layers is partly due to the increased impurity levels evolving from its doping, size effects play a primary role in limiting the thermal conductivity of GaN layers tens of microns thick. Device layers, even of pristine quality, will therefore exhibit thermal conductivities less than the bulk value of 240 W/mK owing to their finite thickness.