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We demonstrate a substantial modulation of the optical properties of multilayer graphene (∼100 layers) using a simple device consisting of a multilayer graphene/polymer electrolyte membrane/gold film stack. Applying a voltage of 3–4 V drives the intercalation of anion [TFSI]− [ion liquid diethylmethyl(2-methoxyethyl)ammonium bis(trifluoromethylsulfonyl)imide [DEME][TFSI]] resulting in the reversible modulation of the properties of this optically dense material. Upon intercalation, we observe an abrupt shift of 35 cm−1 in the G band Raman mode, an abrupt increase in FTIR reflectance over the wavelength range from 1.67 to 5 μm (2000–6000 cm−1), and an abrupt increase in luminescent background observed in the Raman spectra of graphene. All of these abrupt changes in the optical properties of this material arise from the intercalation of the TFSI− ion and the associated change in the free carrier density (Δn = 1020 cm−3). Suppression of the 2D band Raman mode observed around 3 V corresponds to Pauli blocking of the double resonance Raman process and indicates a modulation of the Fermi energy of ΔEF = 1.1 eV.

 

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. 

As one of the most fundamental physical phenomena, charge density wave (CDW) order predominantly occurs in metallic systems such as quasi‐1D metals, doped cuprates, and transition metal dichalcogenides, where it is well understood in terms of Fermi surface nesting and electron–phonon coupling mechanisms. On the other hand, CDW phenomena in semiconducting systems, particularly at the low carrier concentration limit, are less common and feature intricate characteristics, which often necessitate the exploration of novel mechanisms, such as electron–hole coupling or Mott physics, to explain. In this study, an approach combining electrical transport, synchrotron X‐ray diffraction, and density‐functional theory calculations is used to investigate CDW order and a series of hysteretic phase transitions in a diluted‐band semiconductor, BaTiS₃. These experimental and theoretical findings suggest that the observed CDW order and phase transitions in BaTiS₃may be attributed to both electron–phonon coupling and non‐negligible electron–electron interactions in the system. This work highlights BaTiS₃as a unique platform to explore CDW physics and novel electronic phases in the dilute filling limit and opens new opportunities for developing novel electronic devices.

 

Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.