Enabling static random-access memory cell scaling with monolithic 3D integration of 2D field-effect transistors
Static Random-Access Memory (SRAM) cells are fundamental in computer architecture, serving crucial roles in cache memory, buffers, and registers due to their high-speed performance and low power consumption. However, scaling SRAM cells to advanced technology nodes poses significant challenges. Three-dimensional (3D) integration offers a promising solution for reinstating SRAM scaling by vertically stacking devices, thereby reducing the physical footprint. In this study, we demonstrate approximately 40% reduction in cell area and improved interconnect length for 3D SRAM cells constructed from field-effect transistors (FETs) based on monolayer MoS<sub>2</sub>, compared to the planar design. Using the layout for the 450 nm technology node, our 2-tier 3D SRAM design achieves better integration density than the planar 350 nm node. Furthermore, we project up to 70% reduction in cell area for 3-tier 3D SRAM cells, closely matching the cell area of the planar 250 nm node. We have successfully realized 1 kilobit of planar SRAM and 2-tier 3D SRAM cell arrays occupying areas of 0.0358 mm² and 0.0251 mm², respectively, each comprising 6144 MoS<sub>2</sub> FETs. Finally, we project the footprint advantage for 3D SRAM cells at scaled technology nodes. Our demonstration highlights the potential of 3D integration of 2D FETs in advancing SRAM technology.
Authors
- Saptarshi Das (ORCID: https://orcid.org/0000-0002-0188-945X)
- Joan Marie Redwing (ORCID: https://orcid.org/0000-0002-7906-452X)
- Andrew R. Graves (ORCID: https://orcid.org/0000-0001-8711-8279)
- Lei Ding (ORCID: https://orcid.org/0000-0001-7382-2679)
- Shiva Subbulakshmi Radhakrishnan (ORCID: https://orcid.org/0000-0003-1136-7425)
- Muhtasim Ul Karim Sadaf (ORCID: https://orcid.org/0000-0002-1673-9497)
- Z. Chen (ORCID: https://orcid.org/0000-0001-9033-7245)
- Yang Yang (ORCID: https://orcid.org/0000-0002-0025-5914)
- Yongwen Sun (ORCID: https://orcid.org/0009-0007-9427-035X)
Institutions
- Pennsylvania State University (US)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-05-26
- DOI
- https://doi.org/10.1038/s41467-025-59993-8
- Citations
- 15
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 10.96
Funders
- National Science Foundation
- University of Pennsylvania
- Pennsylvania State University
- Office of Naval Research
- Division of Materials Research
- Division of Electrical, Communications and Cyber Systems