Floating gate (FG) memory has long erasing time, which limits its application as an electronic synapse in online training. This paper proposes a novel enhanced floating gate memory (EFM) by TCAD simulation. Here, three other structures are simulated just for comparison. The simulation results show that the erasing speed is about 34ns while the other three need the time over 1.8ms, which makes the operation speed of long-term potentiation (LTP) more symmetrical to long-term depression (LTD). In addition, both LTP and LTD are approximately linear in the simulation results. The speed, linearity, and symmetry of weight update are the keys to online training of analog neural networks. These excellent performances indicated a potential application of EFM in analog neuro-inspired computing.

中文翻译：

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用于模拟神经网络在线训练的增强型浮栅存储器

浮动门（FG）记忆具有较长的擦除时间，这限制了其在在线训练中作为电子突触的应用。本文提出了一种通过 TCAD 模拟的新型增强型浮栅存储器 (EFM)。在这里，模拟了其他三种结构只是为了比较。仿真结果表明，擦除速度约为34ns，而其他三个需要1.8ms以上的时间，这使得长时电位（LTP）的运行速度与长时抑制（LTD）更对称。此外，LTP 和 LTD 在仿真结果中都近似线性。权重更新的速度、线性和对称性是模拟神经网络在线训练的关键。这些优异的性能表明 EFM 在模拟神经启发计算中的潜在应用。