基于三终端MoS2同晶体管的大型横杆阵列
Thomas F Schranghamer1, Andrew Pannone1, Jishnu M Kumar1
1Engineering Science and Mechanics, Penn State University, University Park, PA, USA.
Nature communications
|October 29, 2025
概括
大规模交叉阵列中的三终端MoS2记忆电晶体管可以在不需要重新训练的情况下解决AI推断模. 这些高效,低功耗的设备为边缘AI应用提供了高产量和长时间的保留时间.
科学领域:
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
- 设备工程 设备工程
背景情况:
- 记忆式交叉架构是边缘AI的关键,但与类似的输入扎.
- 对于资源有限的边缘设备,对于输出模两可的现有解决方案通常是不切实际的.
研究的目的:
- 开发和演示大规模的MoS2 memtransistor交叉条数组,以实现高效的边缘AI推断.
- 为了表明memtransistors中的门控制可以在不需要重新训练的情况下解决推断模糊性.
主要方法:
- 制造密集的,大规模的横杆阵列,最多有2048个MoS2晶体管.
- 记忆晶体管性能的表征:产量,写入能量,读取边缘和保留.
- 使用MNIST手写数字分类验证数组性能.
主要成果:
- 在多个大规模阵列中实现了>92%的收益率.
- 已证明的低写能 (~0.2 fJ),高读取率 (10^5),预计保留时间>3年.
- 通过网关调制和分类的MNIST数字成功解决了推理模糊性.
结论:
- 在边缘AI中,MoS2记忆晶体管交叉杆架构为解决推理模两可的解决方案提供了可行的解决方案.
- 门调制提供了一种有效的,不需要重新训练的方法来提高可分离性.
- 这项工作为分散的AI应用程序推进了内存计算.
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