Brain-inspired neuromorphic computing has the potential to revolutionize the current computing paradigm with its massive parallelism and potentially low power consumption. However, the existing approaches of using digital complementary metal-oxide-semiconductor devices (with "0" and "1" states) to emulate gradual/analog behaviors in the neural network are energy intensive and unsustainable; furthermore, emerging memristor devices still face challenges such as nonlinearities and large write noise. Here, an electrochemical graphene synapse, where the electrical conductance of graphene is reversibly modulated by the concentration of Li ions between the layers of graphene is presented. This fundamentally different mechanism allows to achieve a good energy efficiency (<500 fJ per switching event), analog tunability (>250 nonvolatile states), good endurance, and retention performances, and a linear and symmetric resistance response. Essential neuronal functions such as excitatory and inhibitory synapses, long-term potentiation and depression, and spike timing dependent plasticity with good repeatability are demonstrated. The scaling study suggests that this simple, two-dimensional synapse is scalable in terms of switching energy and speed.

these machines offer computational capabilities on the peta-flop scale, making the brain a truly extraordinarily efficient device. [1] One of the major causes of this disparity in energy usage is what is referred to as the von Neumann bottleneck. [3] In modern computing systems, the dedicated central processing units (CPUs) are physically separated from the main memory areas. In addition, these CPUs are programmed to execute operations sequentially, where relevant information needs to be shuttled back and forth between the CPU and the memory. [4] This shuttling of bits puts an inherent cap on the speed of computations, as well as drastically increasing the energy usage.For this reason, researchers are motivated to develop neuromorphic computing systems that can rival or even exceed the cognitive capabilities and energy efficiency of the human brain. As biological systems use complicated systems of networks, all of which work together to form the nervous system, [5] it is going to take a similar multidisciplinary effort for neuromorphic computing to evolve to the point of emulating or even surpassing the human brain, with a concerted approach from material scientists, device engineers, circuit designers, and computer architecture engineers, etc. One particularly exciting facet of this grand work is the synapse used in the neural network. These synapses are capable of both storing information and performing complex operations at the same location, allowing networks to carry out computations in a massively parallel framework, reducing the energy cost per operation. [6] In this pursuit, artificial neural networks (ANNs) have been developed and successfully applied in various fields including: image and pattern recognition, [7] speech recognition, [8] machine translation, [9] and beating humans at chess and recently, Go. [10] Despite these recent strides in neuromorphic computing, the hardware implementation of these ANNs have been hampered by the fact that the digital transistors, the basic computing unit of modern computers, do not behave in the same manner as the analog synapses, the basic building block of the biological neural network. In this paper, we will review a number of different approaches currently being investigated that aim to improve the performance of synaptic devices towards the hardware acceleration of ANNs. First, we will discuss phase change memory (PCM) based synaptic devices, followed by three types In today's era of big-data, a new computing paradigm beyond today's von-Neumann architecture is needed to process these large-scale datasets efficiently. Inspired by the brain, which is better at complex tasks than even supercomputers with much better efficiency, the field of neuromorphic computing has recently attracted immense research interest and can have a profound impact in next-generation computing. Unlike modern computers that use digital "0" and "1" for computation, biological neural networks exhibit analog changes in synaptic connections during the decision-making and learning processes....

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This paper reports the first known investigation of power dissipation and electrical breakdown in aerosol-jet-printed (AJP) graphene interconnects. The electrical performance of aerosol-jet printed (AJP) graphene was characterized using the Transmission Line Method (TLM). The electrical resistance decreased with increasing printing pass number (n); the lowest sheet resistance measured was 1.5 kΩ/sq. for n = 50. The role of thermal resistance (RTH) in power dissipation was studied using a combination of electrical breakdown thermometry and infrared (IR) imaging. A simple lumped thermal model () and COMSOL Multiphysics was used to extract the total RTH, including interfaces. The RTH of AJP graphene on Kapton is ~27 times greater than that of AJP graphene on Al2O3 with a corresponding breakdown current density 10 times less on Kapton versus Al2O3.

By reversibly intercalating ions between the layers of two‐dimensional graphene, Feng Xiong and co‐workers at Pitt develop a novel artificial synapse for neuromorphic computing, as described in article number https://doi.org/10.1002/adma.201802353. With over 250 tunable analog states, good energy efficiency, and promising scalability, these electrochemical synapses can lead to the hardware implementation of neural networks and hence the prevalent use of artificial intelligence.