Prediction of single event upset critical charge and sensitive volume depth by energy deposition analysis of low energy protons

2020 ◽  
pp. 1-16
Author(s):  
Wen Zhao ◽  
Wei Chen ◽  
Chaohui He ◽  
Rongmei Chen ◽  
Liang Wang ◽  
...  
Keyword(s):  
Energy Deposition ◽  
Low Energy ◽  
Sensitive Volume ◽  
Single Event Upset ◽  
Single Event ◽  
Critical Charge

scholarly journals Single Event Upset Mechanisms for Low-Energy-Deposition Events in SiGe HBTs

2008 ◽  
Vol 55 (3) ◽  
pp. 1581-1586 ◽  
Author(s):  
Enrique J. Montes ◽  
Robert A. Reed ◽  
Jonathan A. Pellish ◽  
Michael L. Alles ◽  
Ronald D. Schrimpf ◽  
...  
Keyword(s):  
Energy Deposition ◽  
Low Energy ◽  
Single Event Upset ◽  
Single Event

scholarly journals Influence of Orbital Parameters on SEU Rate of Low-Energy Proton in Nano-SRAM Device

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2030
Author(s):  
Bing Ye ◽  
Li-Hua Mo ◽  
Tao Liu ◽  
You-Mei Sun ◽  
Jie Liu
Keyword(s):  
Heavy Ion ◽  
High Energy ◽  
Low Energy ◽  
Single Event Upset ◽  
Single Event ◽  
High Energy Proton ◽  
Orbital Parameters ◽  
Energy Proton ◽  
Orbital Height ◽  
Selection Of

The on-orbit single-event upset (SEU) rate of nanodevices is closely related to the orbital parameters. In this paper, the on-orbit SEU rate (OOSR) induced by a heavy ion (HI), high-energy proton (HEP) and low-energy proton (LEP) for a 65 nm SRAM device is calculated by using the software SPACE RADIATION under different orbits based on the experimental data. The results indicate that the OOSR induced by the HI, HEP and LEP varies with the orbital parameters. In particular, the orbital height, inclination and shieling thickness are the key parameters that affect the contribution of the LEP to the total OOSR. Our results provide guidance for the selection of nanodevices on different orbits.

Low Energy Proton Single-Event-Upset Test Results on 65 nm SOI SRAM

2008 ◽  
Vol 55 (6) ◽  
pp. 3394-3400 ◽  
Author(s):  
David F. Heidel ◽  
Paul W. Marshall ◽  
Kenneth A. LaBel ◽  
James R. Schwank ◽  
Kenneth P. Rodbell ◽  
...  
Keyword(s):  
Low Energy ◽  
Test Results ◽  
Single Event Upset ◽  
Single Event ◽  
Energy Proton

Single-Event Upset Prediction in SRAMs Account for On-Transistor Sensitive Volume

2015 ◽  
Vol 62 (6) ◽  
pp. 3207-3215 ◽  
Author(s):  
Wang Tianqi ◽  
Liyi Xiao ◽  
Mingxue Huo ◽  
Bin Zhou ◽  
Qi Chunhua ◽  
...  
Keyword(s):  
Sensitive Volume ◽  
Single Event Upset ◽  
Single Event

Single-Event-Upset Critical Charge Measurements and Modeling of 65 nm Silicon-on-Insulator Latches and Memory Cells

2006 ◽  
Vol 53 (6) ◽  
pp. 3512-3517 ◽  
Author(s):  
David F. Heidel ◽  
Kenneth P. Rodbell ◽  
Phil Oldiges ◽  
Michael S. Gordon ◽  
Henry H. K. Tang ◽  
...  
Keyword(s):  
Silicon On Insulator ◽  
Memory Cells ◽  
Single Event Upset ◽  
Single Event ◽  
Critical Charge

scholarly journals Lineal Energy of Proton in Silicon by a Microdosimetry Simulation

2021 ◽  
Vol 11 (3) ◽  
pp. 1113
Author(s):  
Yueh Chiang ◽  
Cher Ming Tan ◽  
Chuan-Jong Tung ◽  
Chung-Chi Lee ◽  
Tsi-Chian Chao
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Energy Deposition ◽  
Semiconductor Industry ◽  
Physical Models ◽  
Sensitive Volume ◽  
Single Event ◽  
Lineal Energy ◽  
Single Event Effect ◽  
Stochastic Event

Single event upset, or Single Event Effect (SEE) is increasingly important as semiconductor devices are entering into nano-meter scale. The Linear Energy Transfer (LET) concept is commonly used to estimate the rate of SEE. The SEE, however, should be related to energy deposition of each stochastic event, but not LET which is a non-stochastic quantity. Instead, microdosimetry, which uses a lineal calculation of energy lost per step for each specific track, should be used to replace LET to predict microelectronic failure from SEEs. Monte Carlo simulation is used for the demonstration, and there are several parameters needed to optimise for SEE simulation, such as the target size, physical models and scoring techniques. We also show the thickness of the sensitive volume, which also correspond to the size of a device, will change the spectra of lineal energy. With a more comprehensive Monte Carlo simulation performed in this work, we also show and explain the differences in our results and the reported results such as those from Hiemstra et al. which are commonly used in semiconductor industry for the prediction of SEE in devices.

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