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，各种形式的数据，如照片，视频和传感器数据，正在以前所未有的速度产生。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，计算能力稳步提升。然而，技术进步正在减少，因为它正在接近物理极限。在这方面，非常需要能够克服现有计算硬件的挑战的替代计算范例。内存计算（IMC）是一种很有前途的方法，它通过直接在内存中执行计算而无需在计算单元和内存之间进行数据传输（这是传统计算机中的关键瓶颈）来显示高性能和能量效率。 然而，为了充分利用IMC，紧凑、节能和高性能的嵌入式非易失性存储器技术是至关重要的。铁电存储器由于其电场驱动的写入机制，具有上级的能量效率。拟议的研究旨在解决铁电存储器中的剩余问题。对于基于电容器的铁电随机存取存储器，探索了能够准无损地感测极化的器件设计。对于铁电场效应晶体管，后端线兼容的金属氧化物沟道和双端口结构，采用了优良的耐久性，读取干扰自由的功能，和最小的电荷陷阱。还探讨了所提出的设备的内存计算应用。与研究工作紧密结合的是5年的教育活动。通过为K-12和社区大学的学生和教师提供的讲座和实践经验，我们的目标是促进兴奋，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，特别是那些代表性不足的群体。该奖项反映了NSF的法定使命，通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。