Enhancing Thermal Interface Conductance to Graphene Using Ni-Pd Alloy Contacts.

To identify superior thermal contacts to graphene, we implement a high-throughput methodology that systematically explores the Ni-Pd alloy composition spectrum and the effect of Cr adhesion layer thickness on thermal interface conductance with monolayer graphene. Frequency domain thermoreflectance measurements of two independently prepared Ni-Pd/Cr/graphene/SiO2 samples identify a maximum metal/graphene/SiO2 junction thermal interface conductance of 114 ± (39, 25) MW/m2 K and 113 ± (33, 22) MW/m2 K at ∼10 at. % Pd in Ni-nearly double the highest reported value for pure metals and 3 times that of pure Ni or Pd. The presence of Cr, at any thickness, suppresses this maximum. Although the origin of the peak is unresolved, we find that it correlates with a region of the Ni-Pd phase diagram that exhibits a miscibility gap. Cross-sectional imaging by high-resolution transmission electron microscopy identifies striations in the alloy at this particular composition, consistent with separation into multiple phases. Through this work, we draw attention to alloys in the search for better contacts to two-dimensional materials for next-generation devices.

Low-Power, Electrochemically Tunable Graphene Synapses for Neuromorphic Computing.

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.