Unlocking neuromorphic memory through functional device

doi:10.1016/j.device.2025.100875

2025-09-01

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DOI：10.1016/j.device.2025.100875

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摘要

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实现高能效和快速处理速度仍是人工智能应用中的关键挑战。神经形态计算（NC），以其模拟生物神经网络的特点，为开发能效高、高性能的系统提供了一种有前景的方法。新兴的存储器件，包括浮栅晶体管（FGTs）、铁电晶体管和电阻式随机存取存储器（RRAM），因其能有效模拟神经元和突触功能而引起了广泛关注。在此，我们讨论存储技术的发展及其未来在神经形态系统中的潜在集成。

期刊

Device

IF:

论文数:

148

被引数:

745

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