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Edge devices face challenges when implementing deep neural networks due to constraints on their computational resources and power consumption. Fuzzy logic systems can potentially provide more efficient edge implementations due to their compactness and capacity to manage uncertain data. However, their hardware realization remains difficult, primarily because implementing reconfigurable membership function generators using conventional technologies requires high circuit complexity and power consumption. Here we report a multigate van der Waals interfacial junction transistor based on a molybdenum disulfide/graphene heterostructure that can generate tunable Gaussian-like and π-shaped membership functions. By integrating these generators with peripheral circuits, we create a reconfigurable fuzzy controller hardware capable of nonlinear system control. This fuzzy logic system can also be integrated with a few-layer convolution neural network to form a fuzzy neural network with enhanced performance in image segmentation. 

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 As one of the most fundamental physical phenomena, charge density wave (CDW) order predominantly occurs in metallic systems such as quasi‐one‐dimensional (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, we combined electrical transport, synchrotron X‐ray diffraction and density‐functional theory (DFT) calculations to investigate CDW order and a series of hysteretic phase transitions in a dilute d ‐band semiconductor, BaTiS 3 . Our experimental and theoretical findings suggest that the observed CDW order and phase transitions in BaTiS 3 may be attributed to both electron‐phonon coupling and non‐negligible electron‐electron interactions in the system. Our work highlights BaTiS 3 as a unique platform to explore CDW physics and novel electronic phases in the dilute filling limit and could open new opportunities for developing novel electronic devices. This article is protected by copyright. All rights reserved 

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.