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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. 

The convergence of topology and correlations represents a highly coveted realm in the pursuit of novel quantum states of matter [1, 2]. Introducing electron correlations to a quantum spin Hall (QSH) insulator can lead to the emergence of a fractional topological insulator and other exotic time-reversal-symmetric topological order [3– 10], not possible in quantum Hall and Chern insulator systems. However, the QSH insulator with quantized edge conductance remains rare, let alone that with significant correlations. In this work, we report a novel dual QSH insulator within the intrinsic monolayer crystal of TaIrTe4, arising from the interplay of its single-particle topology and density-tuned electron correlations. At charge neutrality, monolayer TaIrTe4 demonstrates the QSH insulator that aligns with single-particle band structure calculations, manifesting enhanced nonlocal transport and quantized helical edge conductance. Interestingly, upon introducing electrons from charge neutrality, TaIrTe4 only shows metallic behavior in a small range of charge densities but quickly goes into a new insulating state, entirely unexpected based on TaIrTe4’s single-particle band structure. This insulating state could arise from a strong electronic instability near the van Hove singularities (VHS), likely leading to a charge density wave (CDW). Remarkably, within this correlated insulating gap, we observe a resurgence of the QSH state, marked by the revival of nonlocal transport and quantized helical edge conduction. Our observation of helical edge conduction in a CDW gap could bridge spin physics and charge orders. The discovery of a dual QSH insulator introduces a new method for creating topological flat minibands via CDW superlattices, which offer a promising platform for exploring time-reversal-symmetric fractional phases and electromagnetism [3–5, 11, 12]. 

Advances in algorithms and low-power computing hardware imply that machine learning is of potential use in off-grid medical data classification and diagnosis applications such as electrocardiogram interpretation. However, although support vector machine algorithms for electrocardiogram classification show high classification accuracy, hardware implementations for edge applications are impractical due to the complexity and substantial power consumption needed for kernel optimization when using conventional complementary metal–oxide–semiconductor circuits. Here we report reconfigurable mixed-kernel transistors based on dual-gated van der Waals heterojunctions that can generate fully tunable individual and mixed Gaussian and sigmoid functions for analogue support vector machine kernel applications. We show that the heterojunction-generated kernels can be used for arrhythmia detection from electrocardiogram signals with high classification accuracy compared with standard radial basis function kernels. The reconfigurable nature of mixed-kernel heterojunction transistors also allows for personalized detection using Bayesian optimization. A single mixed-kernel heterojunction device can generate the equivalent transfer function of a complementary metal–oxide–semiconductor circuit comprising dozens of transistors and thus provides a low-power approach for support vector machine classification applications. 

Abstract Despite significant progress in solution‐processing of 2D materials, it remains challenging to reliably print high‐performance semiconducting channels that can be efficiently modulated in a field‐effect transistor (FET). Herein, electrochemically exfoliated MoS₂nanosheets are inkjet‐printed into ultrathin semiconducting channels, resulting in high on/off current ratios up to 10³. The reported printing strategy is reliable and general for thin film channel fabrication even in the presence of the ubiquitous coffee‐ring effect. Statistical modeling analysis on the printed pattern profiles suggests that a spaced parallel printing approach can overcome the coffee‐ring effect during inkjet printing, resulting in uniform 2D flake percolation networks. The uniformity of the printed features allows the MoS₂channel to be hundreds of micrometers long, which easily accommodates the typical inkjet printing resolution of tens of micrometers, thereby enabling fully printed FETs. As a proof of concept, FET water sensors are demonstrated using printed MoS₂as the FET channel, and printed graphene as the electrodes and the sensing area. After functionalization of the sensing area, the printed water sensor shows a selective response to Pb²⁺in water down to 2 ppb. This work paves the way for additive nanomanufacturing of FET‐based sensors and related devices using 2D nanomaterials.