晶圆尺度Ag2基于S的Memristive交叉阵列,具有超低的切换能量,可以达到生物突触
Yuan Zhu1, Tomas Nyberg1, Leif Nyholm2
1Division of Solid-State Electronics, Department of Electrical Engineering, Uppsala University, 75121, Uppsala, Sweden.
Nano-micro letters
|November 21, 2024
概括
研究人员开发了使用硫化银 (Ag2S) 进行神经形态计算的节能记忆交叉阵列 (MCAs). 这些MCA以超低的切换能量模仿生物突触,使先进的AI应用成为可能.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 记忆交叉阵列 (MCAs) 对于节能的神经形态计算至关重要.
- 由于缓慢的memristor动力学,当前配合金属氧化物半导体 (CMOS) 的MCAs消耗大量能量.
- 弥合人造和生物突触之间的能量差距仍然是一个关键的挑战.
研究的目的:
- 开发晶圆尺度的记忆交叉阵列 (MCAs),其能耗可与生物突触相提并论.
- 为了使CMOS兼容的MCAs在低温下进行制造,用于更广泛的应用.
- 展示这些MCAs在像图像识别这样的深度学习任务中的潜力.
主要方法:
- 在160°C以下使用CMOS兼容工艺制造基于Ag2S的MCAs.
- 优化Ag2S电解质微结构以增强Ag+离子迁移.
- 将MCAs与高级培训算法集成,以弥补memristor的非理想性.
主要成果:
- 实现了基于Ag2S的晶圆尺度MCAs,其创纪录的低切换值约为-0.1V.
- 证明了在femtjoule范围内的超低切换能量,与生物突触相当.
- 在使用开发的MCAs的图像识别模拟中获得了92.6%的准确性.
结论:
- 基于Ag2S的MCAs为实现超高能效神经形态计算提供了一个有前途的途径.
- 低温制造使其能够与灵活的电子产品集成.
- 先进的训练算法有效地弥补了设备的非理想性,释放了深度学习潜力.
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