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- W4381330451 abstract "Abstract This work reports on the hardware implementation of analog dot-product operation on arrays of two-dimensional (2D) hexagonal boron nitride (h-BN) memristors. This extends beyond previous work that studied isolated device characteristics towards the application of analog neural network accelerators based on 2D memristor arrays. The wafer-level fabrication of the memristor arrays is enabled by large-area transfer of CVD-grown few-layer (8 layers) h-BN films. Individual devices achieve an on/off ratio of >10, low voltage operation (∼0.5 V set / V reset ), good endurance (>6000 programming steps), and good retention (>10 4 s). The dot-product operation shows excellent linearity and repeatability, with low read energy consumption (∼200 aJ to 20 fJ per operation), with minimal error and deviation over various measurement cycles. Moreover, we present the implementation of a stochastic logistic regression algorithm in 2D h-BN memristor hardware for the classification of noisy images. The promising resistive switching characteristics, performance of dot-product computation, and successful demonstration of logistic regression in h-BN memristors signify an important step towards the integration of 2D materials for next-generation neuromorphic computing systems." @default.
- W4381330451 created "2023-06-21" @default.
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- W4381330451 date "2023-06-29" @default.
- W4381330451 modified "2023-10-10" @default.
- W4381330451 title "Dot-product computation and logistic regression with 2D hexagonal-boron nitride (h-BN) memristor arrays" @default.
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- W4381330451 doi "https://doi.org/10.1088/2053-1583/acdfe1" @default.
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