Solid-state iontronic devices for advanced data processing

点击翻译按钮获取中文摘要

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.