In this work, a printable tungsten disulfide (WS2) based ink is developed from readily available WS2 powder (0.6 µm average particle size), and an ink-jet printing based deposition method for a tungsten disulfide film is presented. WS2 flake coverage and bulk electrical characteristics under three different irradiance conditions are examined and discussed. Presence of excitons in the absorbance of the inks is performed by optical UV-Vis spectrometry. Metrics using the A exciton peak generated by the few-layered flakes are used to calculate the average flake lateral dimensions, the concentration of WS2 in the inks after size selection and filtering, as well as the average monolayer count of the flakes. After printing, scanning electron microscopy is used to confirm average flake lateral size and average flake area coverage, while an atomic force microscope is used to confirm flake thickness.

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Many currently available hardware implementations of the unsupervised self-organizing feature map (SOFM) algorithm utilize CMOS-only circuits that often compromise key behaviors of the SOFM algorithm due to complexity. We propose a neuromorphic architecture harnessing the unique properties of FeFETs and gated-RRAM for in-memory computing to implement the SOFM algorithm. The FeFET-based synapse, organized in a novel circuit, is able to compute the input-weight Euclidean error in memory via the saturation drain current. The selfdecaying states of the gated-RRAM allow for a self-decaying neighborhood and learning rate implementation to allow for convergence and lifelong learning. This novel architecture is able to successfully cluster benchmarks (RGB colors and MNIST handwritten digits) and real-life datasets such as COVID-19 patient chest x-rays completely unsupervised. The architecture also demonstrates a significant amount of robustness to device variability and damaged neurons. Additionally, the proposed architecture is completely parallelized and provides a power efficient platform for implementing the SOFM algorithm.

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