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: 2239284
• 负责人: Kai Ni
• 金额: $ 54.99万
• 依托单位: Rochester Institute of Tech
• 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: 2023-02-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239284&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，照片、视频和传感器数据等各种形式的数据正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，计算能力一直在稳步提高。然而，技术收益正在减少，因为它正在接近物理极限。在这方面，可以克服现有计算硬件挑战的替代计算范例是非常可取的。内存计算(IMC)是一种很有前途的方法，它通过在内存中直接执行计算而不需要在计算单元和内存之间进行数据传输来显示高性能和能源效率，这是传统计算机中的一个关键瓶颈。然而，要充分利用IMC，紧凑、节能、高性能的嵌入式非易失性存储技术至关重要。提出的研究旨在利用最近发现的铁电HfO2来开发这样的存储技术。由于其电场驱动写入机制，铁电存储显示出优异的能量效率。这项拟议的研究旨在解决铁电存储器的剩余问题。对于基于电容的铁电随机存取存储器，探索了能够准无损地检测极化的器件设计。对于铁电场效应晶体管，采用后端兼容的金属氧化物沟道和双端口结构，具有出色的耐久性、无读干扰特性和最小的电荷陷阱。还探讨了所提出的设备的内存计算应用。与研究工作紧密相连的是5年教育活动。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，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，特别是那些代表性不足的群体。模糊不同学科之间界限的课程开发是为未来的硬件开发而提出的。这一奖项反映了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。

项目成果

Quasi-Nondestructive Read Out of Ferroelectric Capacitor Polarization by Exploiting a 2TnC Cell to Relax the Endurance Requirement

利用 2TnC 单元准无损读出铁电电容器极化以放宽耐久性要求

• DOI: 10.1109/led.2023.3290940
• 发表时间: 2023
• 期刊: IEEE Electron Device Letters
• 影响因子: 4.9
• 作者: Xiao, Yi;Deng, Shan;Zhao, Zijian;Faris, Zubair;Xu, Yixin;Huang, Tzu-Jung;Narayanan, Vijaykrishnan;Ni, Kai
• 通讯作者: Ni, Kai

A Compact Ferroelectric 2T-(n+1)C Cell to Implement AND-OR Logic in Memory

用于在存储器中实现 AND-OR 逻辑的紧凑型铁电 2T-(n 1)C 单元

• DOI: 10.1109/isvlsi59464.2023.10238503
• 发表时间: 2023
• 期刊: IEEE
• 作者: Xiao, Yi;Xu, Yixin;Deng, Shan;Zhao, Zijian;George, Sumitha;Ni, Kai;Narayanan, Vijaykrishnan
• 通讯作者: Narayanan, Vijaykrishnan

Computational Associative Memory Powered by Ferroelectric Memory

由铁电存储器驱动的计算联想存储器

• DOI: 10.1109/drc58590.2023.10187048
• 发表时间: 2023
• 期刊: IEEE
• 作者: Ni, Kai;Xiao, Yi;Deng, Shan;Narayanan, Vijaykrishnan
• 通讯作者: Narayanan, Vijaykrishnan