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

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:
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
项目状态:
未结题
起止时间:
2023-10-01 至 2028-04-30

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中文摘要
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英文摘要
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.
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Collaborative Research: FET: Medium:Compact and Energy-Efficient Compute-in-Memory Accelerator for Deep Learning Leveraging Ferroelectric Vertical NAND Memory
  • 批准号:
    2312884
  • 项目类别:
    Standard Grant
  • 资助金额:
    $26.8万
  • 财政年份:
    2023
  • 负责人:
    Kai Ni
  • 依托单位:
Collaborative Research: CMOS+X: A Device-to-Architecture Co-development and Demonstration of Large-scale Integration of FeFET on CMOS for Emerging Computing Applications
  • 批准号:
    2404874
  • 项目类别:
    Standard Grant
  • 资助金额:
    $24.0万
  • 财政年份:
    2023
  • 负责人:
    Kai Ni
  • 依托单位:
Collaborative Research: SHF: Medium: A Comprehensive Modeling Framework for Cross-Layer Benchmarking of In-Memory Computing Fabrics: From Devices to Applications
  • 批准号:
    2347024
  • 项目类别:
    Standard Grant
  • 资助金额:
    $42.82万
  • 财政年份:
    2023
  • 负责人:
    Kai Ni
  • 依托单位:
Collaborative Research: FET: Medium:Compact and Energy-Efficient Compute-in-Memory Accelerator for Deep Learning Leveraging Ferroelectric Vertical NAND Memory
  • 批准号:
    2344819
  • 项目类别:
    Standard Grant
  • 资助金额:
    $26.8万
  • 财政年份:
    2023
  • 负责人:
    Kai Ni
  • 依托单位:
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