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

该奖项支持综合研究，教育和推广工作，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候选设备。忆阻器是一种材料器件，其可以在外场下改变其电阻状态，并且通过主体材料中导电通道的形成和演变来保持这种变化。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实现。在这个项目中，PI和他的团队的目标是开发一个计算框架，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，表面能和机械应变的作用，这些因素可以进行调整，以实现低电流和逐步切换。在此基础上，PI将识别新的移动的物种/主体材料，并预测可以实现渐进、均匀和稳定的开关性能的微结构。这些性能是忆阻器用于准确高效的神经网络训练和模拟计算的关键。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，这些忆阻器将用于进一步对计算方法进行基准测试。为了将教育和外展活动与研究工作结合起来，PI将把开发的计算工具汇编成用户界面软件，并将其分发给更广泛的研究界。该项目将鼓励代表性不足的少数民族，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-学员”方案从事科学和工程相关的项目。K-12外展活动将涉及通过利用德克萨斯大学新生预科工程计划与当地大学的持续合作，并在当地博物馆和图书馆的年度“工程师周”上进行，由全国专业工程师协会推广，以吸引有才华的学生进入STEM领域。技术总结该奖项支持综合研究，教育，以及开发和应用计算模型的推广工作，以了解钌基忆阻器中的新型电阻开关机制，并确定新的移动的物种和宿主材料，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，使得存储器中计算成为可能，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，例如非易失性存储器、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，并且支撑这些转换行为的机制还没有完全理解。本研究的主要目的是建立准确的理论和推进这些非常理想的电阻开关的机制的知识，并确定潜在的移动的物种和主机材料，使这些所需的性能是无法实现与现有的忆阻器。假设还原-氧化反应、电荷传输以及界面和应变效应之间的相互作用导致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的物种和设计开关层的微结构来实现所需的开关性能。该项目将整合实验验证的相场模型，以了解由化学，电学，界面和机械能竞争驱动的渐进和突然切换动力学，高通量计算和机器学习，以识别新的移动的物种/宿主材料，以及主体材料的微观结构的结合的原子和介观模拟及其对电阻的稳定性和均匀性的影响。切换从这项理论研究中获得的知识将指导新型忆阻器的实验合成，表征和测量，这将用于进一步基准和补充模拟工作。为了将教育和外展活动与研究工作结合起来，PI将把开发的计算工具汇编成用户界面软件，并将其分发给更广泛的研究界。该项目将鼓励代表性不足的少数民族，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-学员”方案从事科学和工程相关的项目。K-12外展活动将涉及通过利用德克萨斯大学新生预科工程计划与当地大学继续合作，并在当地博物馆和图书馆举行的年度“工程师周”上进行，由全国专业工程师协会推广，吸引有才华的学生进入STEM领域。该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响评审标准。

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.