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

非技术总结该奖项支持在理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方面的综合研究、教育和推广工作。忆阻器是一种通过主体材料中导电沟道的形成和演化来改变其在外场作用下的阻态并保持这种变化的材料器件。然而，在单个器件中表现出低能量和渐变电阻开关、良好的保持性和开关的一致性的高性能忆阻器还没有实现。在这个项目中，PI和他的团队致力于开发一个计算框架，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，这些因素可以潜在地进行调整，以实现低电流和逐步切换。在此基础上，PI将识别新的可移动物种/主体材料，并预测能够实现渐进、均匀和稳定开关性能的微结构。这些性能是用于准确和高效的神经网络训练和模拟计算的忆阻器的关键。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，并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，国际和平研究所将把开发的计算工具汇编成一个用户界面软件，并将其分发给更广泛的研究界。该项目将鼓励未被充分代表的少数族裔，特别是达拉斯福特沃思堡地区的西班牙裔和第一代大学生，通过拟议的“Mentor−−Mentee”计划，从事与科学和工程相关的项目。K-12推广活动将通过利用德克萨斯大学新生工程预科计划与当地大学继续合作，并在国家专业工程师协会推动的一年一度的当地博物馆和图书馆的“工程师周”上开展，以吸引有才华的学生进入STEM领域。TECHNICAL SUMMARY该奖项支持综合研究、教育和推广工作，开发和应用计算模型，以了解基于Ru的记忆器中新颖的阻性开关机制，并确定新的可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，使其能够进行内存计算，使其成为下一代以数据为中心的计算应用的候选材料，如非易失性存储器、神经网络训练和模拟计算等。然而，表现出低能量和渐变电阻开关、良好的保持和开关一致性的高性能忆阻器还没有实现，支持这些开关行为的机制还不完全清楚。这项研究的主要目的是建立准确的理论，促进对这些非常理想的电阻开关机制的了解，并确定潜在的移动物种和宿主材料，使这些需要的性能是现有的忆阻器无法实现的。假设还原-氧化反应、电荷输运、界面效应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图景，通过选择合适的可移动物种和设计开关层的微结构来实现所需的开关性能。该项目将集成实验验证的相场模型，以了解由化学、电气、界面和机械能竞争驱动的渐进和突然开关动力学，识别新的可移动物种/主体材料的高通量计算和机器学习，以及主体材料微观结构的原子和介观联合建模及其对阻性开关稳定性和一致性的影响。从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，并将用于进一步的基准测试和补充仿真工作。为了将教育和外展活动与研究工作结合起来，国际和平研究所将把开发的计算工具汇编成一个用户界面软件，并将其分发给更广泛的研究界。该项目将鼓励未被充分代表的少数族裔，特别是达拉斯福特沃思堡地区的西班牙裔和第一代大学生，通过拟议的“Mentor−−Mentee”计划，从事与科学和工程相关的项目。K-12外展活动将通过利用德克萨斯州新生前工程计划与当地大学继续合作，并在国家专业工程师协会推动的一年一度的当地博物馆和图书馆的“工程师周”上进行，以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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.