Electrical breakdown response for multiple-gap MEMS structures

We characterize the electrical breakdown response for planar structures, fabricated using microelectromechanical systems (MEMS) methods and materials, to enable design of high voltage microswitches. Electrode configurations that use multiple air gaps provide voltage division between electrodes and allow large voltage holdoff values in microswitch contact configurations with short actuation distances. The comparatively large benefits gained from very small air gaps (4 to 7 um) help to enable high holdoff values, particularly when multiple gaps in this range are added in series. The capacitive effect in multiple gaps can lower breakdown levels, but sufficient electrode spacing reduces this effect. © 2006 IEEE.link_to_subscribed_fulltex

Electrical breakdown response for multiple-gap MEMS structures

We characterize the electrical breakdown response for planar structures, fabricated using microelectromechanical systems (MEMS) methods and materials, to enable design of high voltage microswitches. Electrode configurations that use multiple air gaps provide voltage division between electrodes and allow large voltage holdoff values in microswitch contact configurations with short actuation distances. The comparatively large benefits gained from very small air gaps (4 to 7 um) help to enable high holdoff values, particularly when multiple gaps in this range are added in series. The capacitive effect in multiple gaps can lower breakdown levels, but sufficient electrode spacing reduces this effect. © 2006 IEEE.link_to_subscribed_fulltex

Optimized pulsed write schemes improve linearity and write speed for low-power organic neuromorphic devices

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

Redox transistors for neuromorphic computing

Efficiency bottlenecks inherent to conventional computing in executing neural algorithms have spurred the development of novel devices capable of 'in-memory' computing. Commonly known as 'memristors,' a variety of device concepts including conducting bridge, vacancy filament, phase change, and other types have been proposed as promising elements in artificial neural networks for executing inference and learning algorithms. In this article, we review the recent advances in memristor technology for neuromorphic computing and discuss strategies for addressing the most significant performance challenges, including nonlinearity, high read/write currents, and endurance. As an alternative to two-terminal memristors, we introduce the three-terminal electrochemical memory based on the redox transistor (RT), which uses a gate to tune the redox state of the channel. Decoupling the 'read' and 'write' operations using a third terminal and storage of information as a charge-compensated redox reaction in the bulk of the transistor enables high-density information storage. These properties enable low-energy operation without compromising analog performance and nonvolatility. We discuss the RT operating mechanisms using organic and inorganic materials, approaches for array integration, and prospects for achieving the device density and switching speeds necessary to make electrochemical memory competitive with established digital technology

Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing

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 1 billion write-read operations and support >1-megahertz write-read frequencies

Stabilized open metal sites in bimetallic metal-organic framework catalysts for hydrogen production from alcohols

Liquid organic hydrogen carriers such as alcohols and polyols are a high-capacity means of transporting and reversibly storing hydrogen that demands effective catalysts to drive the (de)hydrogenation reactions under mild conditions. We employed a combined theory/experiment approach to develop MOF-74 catalysts for alcohol dehydrogenation and examine the performance of the open metal sites (OMS), which have properties analogous to the active sites in high-performance single-site catalysts and homogeneous catalysts. Methanol dehydrogenation was used as a model reaction system for assessing the performance of five monometallic M-MOF-74 variants (M = Co, Cu, Mg, Mn, Ni). Co-MOF-74 and Ni-MOF-74 give the highest H2 productivity. However, Ni-MOF-74 is unstable under reaction conditions and forms metallic nickel particles. To improve catalyst activity and stability, bimetallic (NixMg1-x)-MOF-74 catalysts were developed that stabilize the Ni OMS and promote the dehydrogenation reaction. An optimal composition exists at (Ni0.32Mg0.68)-MOF-74 that gives the greatest H2 productivity, up to 203 mL gcat-1 min-1 at 300 °C, and maintains 100% selectivity to CO and H2 between 225-275 °C. The optimized catalyst is also active for the dehydrogenation of other alcohols. DFT calculations reveal that synergistic interactions between the open metal site and the organic linker lead to lower reaction barriers in the MOF catalysts compared to the open metal site alone. This work expands the suite of hydrogen-related reactions catalyzed by MOF-74 which includes recent work on hydroformulation and our earlier reports of aryl-ether hydrogenolysis. Moreover, it highlights the use of bimetallic frameworks as an effective strategy for stabilizing a high density of catalytically active open metal sites. This journal i

Roadmap on energy harvesting materials

Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere