Oxide film devices for AI processing of temporal signals

用于时间信号人工智能处理的氧化膜器件

• 批准号: 2764909
• 金额: --
• 依托单位: Loughborough University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2764909
• 关键词: Oxide; film; devices; AI; processing

It is possible to run machine learning algorithms to classify and predict time-dependent input signals from sensors in application areas such as language processing, environmental, engineering, or medical monitoring. However, high energy consumption associated with the state-of-the-art hardware for neural networks is hindering development of mobile, compact sensors that can be operated stand-alone and offline. This is an opportunity to design devices that use fundamentally new physical approaches to processing neural networks in order to tackle this issue.The goal of this project is experimental development of thin film devices capable of processing time-dependent electrical signals as part of a neural network. The project includes preparation of thin films of oxide materials; application of different characterisation techniques to study the material properties; designing novel electronic devices; testing their performance with respect to the industry standard benchmarks. This research project is at the intersection of artificial intelligence and device physics, and involves collaborative work between academic researchers in Physics, Chemistry, Computer Science and is linked to the EPSRC grants EP/T027479/1 and EP/S032843/1.

可以运行机器学习算法来分类和预测来自语言处理、环境、工程或医疗监测等应用领域的传感器的时间相关输入信号。然而，与最先进的神经网络硬件相关的高能耗正在阻碍可以独立和离线操作的移动的紧凑型传感器的开发。这是一个设计设备的机会，使用全新的物理方法来处理神经网络，以解决这个问题。该项目的目标是实验开发能够处理时间依赖性电信号的薄膜设备，作为神经网络的一部分。该项目包括制备氧化物材料薄膜;应用不同的表征技术研究材料特性;设计新型电子器件;根据行业标准基准测试其性能。该研究项目处于人工智能和设备物理的交叉点，涉及物理，化学，计算机科学学术研究人员之间的合作，并与EPSRC赠款EP/T027479/1和EP/S 032843/1相关。