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  1. A multiscale simulation approach is developed to simulate the contact transport properties between semimetal and a monolayer two-dimensional transition metal dichalcogenide (TMDC) semiconductor. The results elucidate the mechanisms for low contact resistance between semimetal and TMDC semiconductor contacts from a quantum transport perspective. The simulation results compare favorably with recent experiments. Furthermore, the results show that the contact resistance of a bismuth-MoS2contact can be further reduced by engineering the dielectric environment and doping the TMDC material to [Formula: see text]. The quantum transport simulation indicates the possibility to achieve an ultrashort contact transfer length of ∼1 nm, which can allow aggressive scaling of the contact size.

    Free, publicly-accessible full text available July 12, 2023
  2. Graphene nanogap devices emit light due to oxygen in the gap while showing strong negative differential resistance.
    Free, publicly-accessible full text available January 21, 2023
  3. Free, publicly-accessible full text available December 28, 2022
  4. Abstract Neuromorphic hardware implementation of Boltzmann Machine using a network of stochastic neurons can allow non-deterministic polynomial-time (NP) hard combinatorial optimization problems to be efficiently solved. Efficient implementation of such Boltzmann Machine with simulated annealing desires the statistical parameters of the stochastic neurons to be dynamically tunable, however, there has been limited research on stochastic semiconductor devices with controllable statistical distributions. Here, we demonstrate a reconfigurable tin oxide (SnO x )/molybdenum disulfide (MoS 2 ) heterogeneous memristive device that can realize tunable stochastic dynamics in its output sampling characteristics. The device can sample exponential-class sigmoidal distributions analogous to the Fermi-Dirac distribution of physical systems with quantitatively defined tunable “temperature” effect. A BM composed of these tunable stochastic neuron devices, which can enable simulated annealing with designed “cooling” strategies, is conducted to solve the MAX-SAT, a representative in NP-hard combinatorial optimization problems. Quantitative insights into the effect of different “cooling” strategies on improving the BM optimization process efficiency are also provided.
    Free, publicly-accessible full text available December 1, 2022