Electron injection in tunneling-oxide-less charge-trap flash memories

This paper presents a physics-based technology computer-aided design framework for electron trapping in tunneling-oxide-less Al2O3/Si3N4 (AN) charge-trap devices and its extension to Al2O3/Si3N4/SiO2 (ANO) stacks. Unlike conventional ANO devices with a SiO2 tunneling oxide, AN devices place the Si3N4 layer in direct contact with the poly-Si channel, requiring a different electron injection model. The model combines Fowler–Nordheim tunneling through Si3N4 with direct channel-to-nitride injection caused by the tunneling-oxide-less gate stack. These two mechanisms are represented using shallow and deep trap-energy levels in the nitride layer. The trap energy Et, trap density Nt, tunneling effective mass m*, and spatial distribution are calibrated by matching simulated programming transients and ID–VG characteristics to measurements from fabricated AN devices. The measurement-calibrated framework accurately predicts the threshold-voltage shift ΔVth and trapped-electron distributions as functions of programming voltage and temperature. It also clarifies how Et, Nt, m*, and the spatial distribution determine the trapping rate, stored charge, and trapping location. Using the same nitride trap parameters, the model is applied to ANO devices with different tunneling-oxide thicknesses and accurately predicts their trapped-electron density distribution and ΔVth characteristics. These results establish tunneling-oxide-less AN devices as effective platforms for extracting intrinsic nitride trap parameters applicable to conventional ANO flash stacks and more complex tunneling-stack designs.

Authors

Institutions

Publication Details

Journal
Applied Physics Letters
Published
2026-10-19
DOI
https://doi.org/10.1063/5.0345638
Primary Topic
Semiconductor materials and devices
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Electron injection in tunneling-oxide-less charge-trap flash memories

Joon Hwang, Joonhyung Cho, Jong‐Ho Lee, Minkyu Park et al.
Applied Physics Letters
Semiconductor materials and devices
article

Electron injection in tunneling-oxide-less charge-trap flash memories

Joon Hwang, Joonhyung Cho, Jong‐Ho Lee, Minkyu Park, Suyoung Park
article en

Abstract

This paper presents a physics-based technology computer-aided design framework for electron trapping in tunneling-oxide-less Al2O3/Si3N4 (AN) charge-trap devices and its extension to Al2O3/Si3N4/SiO2 (ANO) stacks. Unlike conventional ANO devices with a SiO2 tunneling oxide, AN devices place the Si3N4 layer in direct contact with the poly-Si channel, requiring a different electron injection model. The model combines Fowler–Nordheim tunneling through Si3N4 with direct channel-to-nitride injection caused by the tunneling-oxide-less gate stack. These two mechanisms are represented using shallow and deep trap-energy levels in the nitride layer. The trap energy Et, trap density Nt, tunneling effective mass m*, and spatial distribution are calibrated by matching simulated programming transients and ID–VG characteristics to measurements from fabricated AN devices. The measurement-calibrated framework accurately predicts the threshold-voltage shift ΔVth and trapped-electron distributions as functions of programming voltage and temperature. It also clarifies how Et, Nt, m*, and the spatial distribution determine the trapping rate, stored charge, and trapping location. Using the same nitride trap parameters, the model is applied to ANO devices with different tunneling-oxide thicknesses and accurately predicts their trapped-electron density distribution and ΔVth characteristics. These results establish tunneling-oxide-less AN devices as effective platforms for extracting intrinsic nitride trap parameters applicable to conventional ANO flash stacks and more complex tunneling-stack designs.

Applied Physics LettersVol. 129(16)
SK Group (United States) (US), Gachon University (KR), Seoul National University (KR)
Openalex Percentile: Top 23%
Semiconductor materials and devices
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.