启用PZT的MoS2浮动门晶体管:克服博尔兹曼暴政,实现超低能耗,实现高精度神经形态计算
Jing Chen1,2, Ye-Qing Zhu3, Xue-Chun Zhao3
1Institute of Marine Science and Technology, Shandong University, Qingdao, Shandong 266237, China.
Nano letters
|November 6, 2023
概括
研究人员使用支持PZT的MoS2浮动门开发了一种新型晶体管,实现低下值摆动 (SS) 和宽内存窗口,以实现高效的人工智能硬件. 这项创新能够实现高度准确的面部识别.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 传统的场效应晶体管 (FET) 受到限制.
- 博尔兹曼的暴政 博尔兹曼暴政
- 将子值波动 (SS) 限制在60mV/十年.
- 基于FET的神经形态设备具有狭窄的记忆窗口,限制了它们的导电量状态以实现准确的神经形态计算.
研究的目的:
- 提出和演示一个新的 PZT 支持的 MoS2 浮动门晶体管 (PFGT) 配置.
- 在低功耗人工智能应用中克服传统FETs的局限性.
- 为了提高人工突触和神经形态计算的性能.
主要方法:
- 制造一个支持 PZT 的 MoS2 浮动门晶体管 (PFGT).
- 晶体管的电特性,包括下值摆动 (SS) 和内存窗口的描述.
- 基于PFGT的人工神经网络的面部识别的实施.
主要成果:
- 在PFGT显示低SS的46mV/十年.
- 在双扫电压范围 (-7~7V) 中,实现了7.2V的宽内存窗口.
- 基于PFGT的神经网络实现了97.3%的面部识别准确度.
结论:
- 开发的PFGT克服了使用的PFGT.
- 博尔兹曼的暴政 博尔兹曼暴政
- 并且提供了一个广泛的内存窗口,适合节能的人工突触.
- 这项工作为先进的低SS晶体管和高效的神经形态计算设备铺平了道路.
- 该PFGT显示了下一代AI硬件使用二维材料的巨大潜力.
相关概念视频
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