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• W2963421388 abstract "We present a fully CMOS-compatible multilevel non-volatile memory technology, without any special process cost. It is especially suitable for storing the weights of artificial neural networks on chip with low cost, high density, and high power-efficiency. We use hot carrier injection to program the single-transistor cells, and we conduct charge pumping experiments which identify interfacial traps, rather than bulk oxide traps, as the dominant factor in producing stable I-V shifts. We also derive a new physics-based experimentally verified logarithmic model to explain the rate of interfacial trap generation in large I-V shift regimes where the conventional power-law no longer applies. We fabricate two chips, one using TSMC's 16 nm FinFET and the other in 28 nm planar, and show the FinFET cells are more favorable for non-volatile memory due to their better channel control. We store multiple levels in each FinFET cell using a “program and check” strategy which sets memory cells' currents with standard deviations less than 2 μA across a shifting range of over 100 μA. We demonstrate 8 level FinFET cells with extrapolated 10-year charge loss within 10% at 125 °C." @default.
• W2963421388 created "2019-07-30" @default.
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• W2963421388 date "2019-09-01" @default.
• W2963421388 modified "2023-10-14" @default.
• W2963421388 title "Fully-CMOS Multi-Level Embedded Non-Volatile Memory Devices With Reliable Long-Term Retention for Efficient Storage of Neural Network Weights" @default.
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• W2963421388 doi "https://doi.org/10.1109/led.2019.2930212" @default.
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