Speech Recognition using Biologically-Inspired Neural Networks
Thomas Bohnstingl, Ayush Garg, et al.
ICASSP 2022
In phase-change memory (PCM), low-field electrical resistance is typically used to quantify the programmed cell state. However, this metric has several disadvantages. First, it exhibits temporal drift, which is a significant challenge for realizing multilevel PCM. Moreover, because of cell-geometry effects, this metric saturates after a certain point and thus masks the fact that the amorphous size increases with increasing input power. Finally, the resistance is typically measured as the current for a fixed bias voltage, which adversely affects the signal-to-noise ratio at high resistance values. A new metric for the programmed state in a PCM cell is proposed that has significant advantages over the resistance metric in all these aspects and is more representative of the fundamental programmed entity, which is the amorphous/crystalline phase configuration in the PCM cell. Analytical and experimental results are presented that demonstrate the efficacy of the proposed metric. © 2011 American Institute of Physics.
Thomas Bohnstingl, Ayush Garg, et al.
ICASSP 2022
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NeurIPS 2023
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IEEE TCAS-II
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Nanotechnology