CAREER: Decipher the Mechanism of High-performance Novel Memristors by Phase-field Simulation

职业：通过相场仿真解读高性能新型忆阻器的机制

• 批准号: 2340595
• 负责人: Ye Cao
• 金额: $ 50.03万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-09-01 至 2029-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340595&HistoricalAwards=false
• 关键词: CAREER; Decipher; Mechanism; performance; Novel

NONTECHNICAL SUMMARYThis award supports integrated research, educational, and outreach efforts on understanding and designing high performance memristors as potential device candidates for next generation data-centric computing applications. Memristor is a type of material device that can change its resistance states under external fields and maintain such changes via the formation and evolution of electrically conductive channels in the host materials. However, high-performance memristors that exhibit low-energy and gradual resistive switching, good retention, and uniformity in switching in a single device have not yet materialized. In this project, the PI and his team aim to develop a computational framework to elucidate the key factors of materials used in memristors as well as the roles of ion migration, electrochemical reaction, surface energy, and mechanical strain that can potentially be tuned to enable low-current and gradual switching. Based on this, the PI will identify new mobile species/host materials and predict microstructures that can achieve gradual, uniform, and stable switching performances. These performances are key to memristors used for accurate and efficient neural network training and analog computing. Knowledge obtained from this study will provide guidance to the experimental synthesis, characterization, and testing of novel memristors, which will be used to further benchmark the computational approach. To integrate the educational and outreach activities with the research works, the PI will compile the developed computational tools into a user-interface software and disseminate it to the broader research community. The project will encourage underrepresented minorities, especially Hispanic and first-generation college students in the Dallas−Fort Worth area, to pursue science and engineering related projects through the proposed “Mentor−Mentee” program. The K-12 outreach activities will involve continued collaborations with local universities through leveraging the Texas Pre-Freshman Engineering Program and be carried out at the annual “Engineers Week” at local museums and libraries, promoted by the National Society of Professional Engineers, to attract talented students into STEM field.TECHNICAL SUMMARYThis award supports integrated research, educational, and outreach efforts on developing and applying computational models to understand a novel resistive switching mechanism in Ruthenium-based memristor, and to identify new mobile species and host materials to achieve needed performance. Memristors, which can change their electrical resistance and maintain such changes, enable in-memory computing, making them promising candidates for next-generation data-centric computing applications, such as nonvolatile memory, neural network training, and analog computing etc. However, high-performance memristors that exhibit low-energy and gradual resistive switching, good retention, and uniformity in switching have not yet been achieved, and the mechanisms that underpin these switching behaviors are not fully understood. The main objective of this research is to establish accurate theories and advance the knowledge of the mechanism of these highly desirable resistive switching, and to identify potential mobile species and host materials that enable these needed performances which are unachievable with existing memristors. It is hypothesized that the interactions among reduction-oxidation reaction, charge transport, and interfacial and strain effects result in a dynamic free energy landscape of the memristor system, which can be engineered by selecting proper mobile species and designing the microstructure of the switching layer to achieve needed switching performance. This project will integrate the experiment-validated phase-field model to understand both gradual and sudden switching dynamics driven by the chemical, electrical, interfacial, and mechanical energy competitions, the high-throughput calculations and machine learning to identify new mobile species/host materials, and the combined atomistic and mesoscale modeling of the microstructure of the host material and its effect on the stability and uniformity of resistive switching. Knowledge obtained from this theoretical study will guide experimental synthesis, characterization, and measurement of the novel memristors, which will be used to further benchmark and complement the simulation work. To integrate the educational and outreach activities with the research works, the PI will compile the developed computational tools into a user-interface software and disseminate it to the broader research community. The project will encourage underrepresented minorities, especially Hispanic and first-generation college students in the Dallas−Fort Worth area, to pursue science and engineering related projects through the proposed “Mentor−Mentee” program. The K-12 outreach activities will involve continued collaborations with local universities through leveraging the Texas Pre-Freshman Engineering Program and be carried out at the annual “Engineers Week” at local museums and libraries, promoted by the National Society of Professional Engineers, to attract talented students into STEM field.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

非技术摘要该奖项支持综合研究、教育和推广工作，以了解和设计高性能忆阻器作为下一代以数据为中心的计算应用的潜在器件候选器件。忆阻器是一种可以在外部场下改变其电阻状态并保持不变的材料器件。这种变化是通过主体材料中导电通道的形成和演变来实现的。然而，在单个器件中表现出低能量和渐进电阻切换、良好保持性和切换均匀性的高性能忆阻器尚未出现。在这个项目中，PI 和他的团队旨在开发一个计算框架，以阐明忆阻器中使用的材料的关键因素，以及离子迁移、电化学反应、表面能和可以调整的机械应变的作用。在此基础上，PI 将识别新的可移动物种/主体材料，并预测可实现渐进、均匀和稳定的开关性能的微结构，这些性能是用于准确、高效神经元的忆阻器的关键。网络培训从这项研究中获得的知识将为新型忆阻器的实验合成、表征和测试提供指导，这将用于进一步对计算方法进行基准测试，以将教育和推广活动与研究工作相结合。将把开发的计算工具编译成用户界面软件，并将其传播给更广泛的研究界，该项目将鼓励少数族裔，特别是达拉斯-沃斯堡地区的西班牙裔和第一代大学生，追求与科学和工程相关的知识。通过拟议的项目“导师-受训者”计划。K-12 外展活动将通过利用德克萨斯大学新生工程项目与当地大学继续合作，并在国家博物馆和图书馆举办的年度“工程师周”上​​开展。专业工程师协会，旨在吸引有才华的学生进入 STEM 领域。技术摘要该奖项支持开发和应用计算模型的综合研究、教育和推广工作，以了解基于钌的新型电阻开关机制忆阻器，并识别新的移动物种和宿主材料以实现所需的性能，忆阻器可以改变其电阻并保持这种变化，从而实现内存计算，使其成为下一代以数据为中心的计算应用的有希望的候选者。如非易失性存储器、神经网络训练和模拟计算等。然而，尚未实现具有低能量、渐进电阻开关、开关保持良好且均匀的高性能忆阻器，以及支持这些开关行为的机制是这项研究的主要目标是建立高度准确的理论并增进对这些理想电阻开关机制的了解，并确定潜在的移动物种和主体材料，以实现现有忆阻器无法实现的这些所需性能。人们再次认识到，氧化还原反应、电荷传输以及界面和应变效应之间的相互作用导致了忆阻器系统的动态自由能景观，这可以通过选择适当的移动物种并设计切换层的微观结构来设计达到该项目将集成经过实验验证的相场模型，以了解由化学、电、界面和机械能竞争、高通量计算和机器学习驱动的渐进和突然切换动力学，以识别新的移动器件。物种/主体材料，以及主体材料微观结构的原子和介观联合建模及其对电阻开关稳定性和均匀性的影响，从这项理论研究中获得的知识将指导新型材料的实验合成、表征和测量。忆阻器，它将用于进一步基准测试和补充模拟工作，为了将教育和推广活动与研究工作相结合，PI 将把开发的计算工具编译成用户界面软件，并将其传播给更广泛的研究界。该项目将鼓励代表性不足的少数族裔，特别是达拉斯-沃斯堡地区的西班牙裔和第一代大学生，通过拟议的“导师-受训者”计划来开展与科学和工程相关的项目。K-12 外展活动将涉及持续合作。与当地大学合作，利用德克萨斯州新生工程项目，并在美国国家专业工程师协会推动的当地博物馆和图书馆一年一度的“工程师周”上​​进行体现，以吸引有才华的学生进入 STEM 领域。该奖项由 NSF 颁发法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。