Solid-state iontronic devices for advanced data processing

用于高级数据处理的固态离子电子设备

• 批准号: RGPIN-2022-03753
• 负责人: Miao, GuoXing
• 金额: $ 3.35万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=751629
• 关键词: Solid; state; iontronic; devices; advanced

Modern electronic devices, such as mass data storage units and logic processing units, are becoming increasingly sophisticated but increasingly power intensive. The constant push to enhance integration/miniaturization and reduce energy footprints requires additional controls and functionalities in novel materials, device structures, and computing architectures. The proposed program aims to apply knowledge from our recent research studies on ion motion in rechargeable ion battery systems to address electronics challenges from a new angle, by mimicking the highly efficient neuron systems used by the human brain which can process a tremendous amount of data in a small form factor with energy consumption equivalent to that used by a modern light bulb. Ionic control is the most natural strategy to explore as neurons fire electrical pulses by opening and closing voltage-controlled ionic channels. Lithium ions, the smallest metal ion with high ionic mobility, have already played a key role in rechargeable battery applications, with the inventors awarded the 2019 Nobel Prize in Chemistry. The PI's team plans to use lithium ions in a drastically different context. They will develop approaches to electrically mobilize these ions to designated locations such as interfaces or bridges to control the device conductance, that is, adding iontronic aspects to existing devices such as resistive or magnetic random access memories (ReRAM and MRAM). Such ionic motions can produce nonvolatile, time dependent or pulse dependent control over these hybrid devices. Adding together, the PI aims to create a complementary metal-oxide-semiconductor (CMOS) compatible, hardware neuromorphic computing platform where the resistive switching synapses adapt to input trigger pulses, and the magnetic tunnel junction neurons fire (flip) at the accumulated currents from the synapses. These form the main objectives of this proposal: ionic MRAM (iMRAM), ReRAM with dedicated ion channels, and their CMOS integration producing a hardware neural network platform that can be readily mass-integrated into more energy efficient neuromorphic computing architectures. Compared to classical computer architectures, this transformative technology links the wealth of knowledge from ionic energy storage and modern integrated electronics, practically adds another dimension of control to existing devices, and allows for smarter, distributed integration under the same space and energy footprints. With Canada already leading in many aspects of artificial intelligence algorisms, this program strengthens Canada's ICT sectors in the hardware departments by directly mimicking the behavior of the human brain and nervous system. The highly interdisciplinary nature of this program also greatly benefits HQP training for Canada, producing next-generation HQP well suited for diverse academic and industrial opportunities in materials science, electronics, nanotechnology, and renewable energy sectors.

诸如大容量数据存储单元和逻辑处理单元之类的现代电子设备正变得越来越复杂，但功率越来越密集。不断推动提高集成度/小型化和减少能量足迹需要在新材料、器件结构和计算架构中增加额外的控制和功能。该计划旨在应用我们最近关于可充电离子电池系统中离子运动的研究知识，从一个新的角度解决电子挑战，通过模仿人类大脑使用的高效神经元系统，该系统可以处理大量的数据，其能量消耗相当于现代灯泡的能量消耗。离子控制是最自然的策略，因为神经元通过打开和关闭电压控制的离子通道来激发电脉冲。锂离子是具有高离子迁移率的最小金属离子，已经在可充电电池应用中发挥了关键作用，其发明者获得了2019年诺贝尔化学奖。PI的团队计划在一个完全不同的环境中使用锂离子。他们将开发方法，将这些离子电移动到指定位置，如接口或桥，以控制设备电导，即在现有设备中添加离子电子方面，如电阻或磁性随机存取存储器（ReRAM和MRAM）。这种离子运动可以对这些混合器件产生非易失性、时间依赖性或脉冲依赖性控制。加在一起，PI旨在创建一个互补金属氧化物半导体（CMOS）兼容的硬件神经形态计算平台，其中电阻开关突触适应输入触发脉冲，磁性隧道结神经元在突触积累的电流下放电（翻转）。这些构成了该提案的主要目标：离子MRAM（iMRAM），具有专用离子通道的ReRAM，以及它们的CMOS集成，从而产生一个硬件神经网络平台，可以很容易地大规模集成到更节能的神经形态计算架构中。与经典的计算机架构相比，这种变革性的技术将离子储能和现代集成电子学的丰富知识联系起来，实际上为现有设备增加了另一个控制维度，并允许在相同的空间和能源足迹下进行更智能的分布式集成。由于加拿大已经在人工智能算法的许多方面处于领先地位，该计划通过直接模仿人脑和神经系统的行为，加强了加拿大硬件部门的ICT部门。该计划的高度跨学科性质也极大地有利于加拿大的HQP培训，产生下一代HQP非常适合材料科学，电子，纳米技术和可再生能源领域的各种学术和工业机会。