journal article·2026-07-10Open access

Quantitative mechanism separation of single-event transients in nanosheet transistors via TCAD simulation

Danlei Liu, Hongxia Liu, Junwei Zhao, Chen Zhong, Shupeng Chen, Shulong Wang

Nanotechnology· Volume 37· Issue 27

Abstract

Abstract As process nodes scale to the nanosheet structure, the continuous reduction in device dimensions and the introduction of vertical stacking structures pose new challenges for single-event effect sensitivity. This work uses calibrated TCAD simulations to systematically investigate the effects of four key parameters—gate length, number of nanosheet stacks, sheet spacing, and drain voltage—on the single-event transient (SET) response of n-type nanosheet devices. By extracting the electron current integrals at the source and drain cross-sections, the relative contributions of the funneling effect and parasitic bipolar junction transistor (BJT) amplification mechanisms are quantitatively distinguished across different transient stages. The results reveal that the SET response exhibits a two-stage behavior: a prompt peak dominated by the funneling effect and modulated by gate length, drain voltage, and sensitive volume, and a delayed tail governed by the parasitic bipolar amplification, which depends on the intrinsic properties of the device structure. Reducing the gate length renders the parasitic BJT effect in the peak stage non-negligible. Increasing the number of stacks induces a nonlinear BJT amplification effect in the tail stage. The impact of sheet spacing on SET response is found to be minor. Drain voltage modulates only the peak stage without altering the tail attenuation characteristics. The two-stage mechanism elucidated in this work provides a physical foundation for radiation-hardened design in gate-all-around devices.
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Research context

OpenAlex topics: Radiation Effects in Electronics · Advancements in Semiconductor Devices and Circuit Design · Semiconductor materials and devices

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Xidian University

Funding

National Natural Science Foundation of China · Research Fund of CIE-SmartChip

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