Physics-Based SPICE-Compatible Compact Model of FLASH Memory With Poly-Si Channel for Computing-in-Memory Applications

Recently, three-dimensional FLASH memory with multi-level cell characteristics has attracted increasing attention to enhance the capabilities of artificial intelligence (AI) by leveraging computingin-memory (CIM) systems. The focus is to maximize the computing performance and design FLASH memory suitable for various AI algorithms, where the memory must achieve a highly controllable multi-level threshold voltage (VT). Therefore, we developed a SPICE compact model that can rapidly simulate charge trap FLASH cells for CIM to identify optimal programming conditions. SPICE simulation results of the transfer characteristics are in good agreement with the results of experimentally fabricated FLASH memory, showing a low error rate of 10%. The model was also validated against the results obtained from the TCAD tool, showing that a consistent VT change was computed in a shorter time than that required using TCAD. Then, the developed model was used to comprehensively investigate how single or multiple gate voltage (VG) pulses affect VT. Moreover, considering recent FLASH memory fabrication processes, we found that grain boundaries in polycrystalline silicon channel materials can be involved in deteriorating gate controllability. Therefore, optimizing the pulse scheme by correcting potential errors identified in advance through fast SPICE simulation can enable the accurate achievement of the specific analog states of the FLASH cells of the CIM architecture, boosting computing performance.

Authors

Institutions

Publication Details

Journal
IEEE Journal of the Electron Devices Society
Published
2024-12-04
DOI
https://doi.org/10.1109/jeds.2024.3511581
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.21
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Physics-Based SPICE-Compatible Compact Model of FLASH Memory With Poly-Si Channel for Computing-in-Memory Applications

Myounggon Kang, Changwook Kim, Donghyun Ryu, Jiyong Woo et al.
1 citations
IEEE Journal of the Electron Devices Society
Advanced Memory and Neural Computing
0.21
article

Physics-Based SPICE-Compatible Compact Model of FLASH Memory With Poly-Si Channel for Computing-in-Memory Applications

Myounggon Kang, Changwook Kim, Donghyun Ryu, Jiyong Woo, Wonjung Kim, Donguk Kim, Yoon Kim, Dae Hwan Kim, Yeonwoo Kim, Jung Rae Cho
article en
1 citations

Abstract

Recently, three-dimensional FLASH memory with multi-level cell characteristics has attracted increasing attention to enhance the capabilities of artificial intelligence (AI) by leveraging computingin-memory (CIM) systems. The focus is to maximize the computing performance and design FLASH memory suitable for various AI algorithms, where the memory must achieve a highly controllable multi-level threshold voltage (VT). Therefore, we developed a SPICE compact model that can rapidly simulate charge trap FLASH cells for CIM to identify optimal programming conditions. SPICE simulation results of the transfer characteristics are in good agreement with the results of experimentally fabricated FLASH memory, showing a low error rate of 10%. The model was also validated against the results obtained from the TCAD tool, showing that a consistent VT change was computed in a shorter time than that required using TCAD. Then, the developed model was used to comprehensively investigate how single or multiple gate voltage (VG) pulses affect VT. Moreover, considering recent FLASH memory fabrication processes, we found that grain boundaries in polycrystalline silicon channel materials can be involved in deteriorating gate controllability. Therefore, optimizing the pulse scheme by correcting potential errors identified in advance through fast SPICE simulation can enable the accurate achievement of the specific analog states of the FLASH cells of the CIM architecture, boosting computing performance.

IEEE Journal of the Electron Devices SocietyVol. 13
Kookmin University (KR), University of Seoul (KR), Seoul National University (KR), Kyungpook National University (KR)
Affordable and clean energy
Openalex Percentile: Top 45%
Advanced Memory and Neural Computing
0.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.