CAREER: High-Performance Ferroelectric Memory for In-Memory Computing

职业：用于内存计算的高性能铁电存储器

• 批准号: 2346953
• 负责人: Kai Ni
• 金额: $ 54.99万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2346953&HistoricalAwards=false
• 关键词: CAREER; Performance; Ferroelectric; Memory; Computing

With the wide deployment of smart devices, various forms of data, such as photos, videos, and sensor data, are being generated at an unprecedented rate. Extracting information from the data for intelligent decision making requires enormous computing power. Following the Moore’s law, the computing power has been steadily improved. However, technology gain is diminishing as it is approaching the physical limits. In this regard, alternative computing paradigms that can overcome the challenges of existing computing hardware are highly desirable. In-memory computing (IMC) is one promising approach that shows high performance and energy efficiency by performing computation directly inside the memory without data transfer between the computing unit and memory, a key bottleneck in conventional computers. However, to fully exploit the IMC, a compact, energy-efficient, and high performance embedded nonvolatile memory technology is critical. The proposed research aims to develop such a memory technology by leveraging recently discovered ferroelectric HfO2.Ferroelectric memory due to its electric field driven write mechanism, exhibits superior energy efficiency. The proposed research aims at addressing the remaining issues in ferroelectric memory. For capacitor-based ferroelectric random access memory, device design that enables quasi-nondestructive sensing of polarization are explored. For ferroelectric field effect transistor, back-end-of-line compatible metal-oxide channel and dual-port structure are adopted for excellent endurance, read disturb free feature, and minimal charge trapping. In-memory computing applications of proposed devices are also explored. Tightly coupled with the research efforts are 5-year education activities. Through the lectures and hands-on experience offered to K-12 and community college students and teachers, the goal is to promote excitement and attract them into the talent pipeline for semiconductor industry. The proposed research will involve undergraduate and graduate students, especially those in under-represented groups. Course development that blurs the boundaries between different disciplines is proposed for hardware development in the future.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.

随着智能设备的广泛部署，正在以前所未有的速率生成各种形式的数据，例如照片，视频和传感器数据。从数据中提取信息以进行智能决策需要巨大的计算能力。遵循摩尔定律，计算能力得到了稳步改善。但是，技术增益正在接近物理限制时减少。在这方面，非常需要克服现有计算硬件挑战的替代计算范例。内存计算（IMC）是一种有望的方法，它通过直接在内存内部执行计算，而无需在计算单元和内存之间进行数据传输，这是一种传统计算机中的关键瓶颈，这表明了高性能和能源效率。但是，要充分探索IMC，至关重要的是紧凑，节能和高性能嵌入的非易失性记忆技术。拟议的研究旨在通过利用最近发现的铁电HFO2。由于其电场驱动器写作机制而引起的铁电气HFO2。拟议的研究旨在解决铁电记忆中的其余问题。对于基于电容器的铁电随机访问存储器，探索了能够启用极化敏感性的设备设计。对于铁电场效应晶体管，采用了后端兼容的金属氧化物通道和双端口结构，以获得出色的耐力，阅读无灾难功能以及最少的电荷捕获。还探索了提出的设备的内存计算应用程序。与研究工作紧密相结合的是5年的教育活动。通过为K-12以及社区大学生和老师提供的讲座和动手经验，目标是促进兴奋并吸引他们进入半导体行业的人才管道。拟议的研究将涉及本科生和研究生，尤其是那些人数不足的群体。将来提出了融合不同学科之间边界的课程开发。未来的硬件开发。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，认为通过评估被认为是珍贵的支持。