Experimental Investigation of Charge Sharing Induced SET Depending on Transistors in Abutted Rows in 65 nm Bulk CMOS Technology

He, Xiyun; Yue, Daheng; Huang, Pengcheng

doi:10.1109/access.2022.3177408

Cited by 1 publication

(3 citation statements)

References 18 publications

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“…During BPNN training, P C1 , P C2 , and P C3 are used as input data. BPNN calculates the transient pulse propagation probability P FF 1 ,prediction through Equations ( 4)- (6). This data will be different from P FF 1 ,simulation .…”

Section: Pulse Propagationmentioning

confidence: 99%

“…It is important to note that since the selected logical instance may affect multiple flip-flops, it is often possible to generate multiple input data for the machine learning model in the second step. Thirdly, the data are inputted into the calibrated BPNN model, and the propagation probability of the transient pulse to a flip-flop is calculated using Equations ( 4)- (6). Fourthly, Equation ( 11) is used to determine whether the transient pulse will be captured by the flip-flop based on the calculated propagation probability.…”

Section: The Ser Evaluation Approachmentioning

confidence: 99%

“…These ionized electron-hole pairs are subject to both drift and diffusion. They move throughout the entire semiconductor material and are collected by transistors [4][5][6]. The collected electron-hole pairs produce unexpected transient pulses in circuit nodes [7].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Soft-Error-Rate Evaluation for Large-Scale Integrated Circuits

Song,

Shao,

Chi

et al. 2023

Electronics

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Transient pulses generated by high-energy particles can cause soft errors in circuits, resulting in spacecraft malfunctions and posing serious threats to the normal operation of spacecraft. For integrated circuits used in space applications, it is necessary to first evaluate soft errors caused by transient pulses. Conventional soft-error-rate evaluation tools are designed to simulate the generation of transient pulses using many accurate models, while the propagation of transient pulses is primarily simulated by circuit-level simulation tools. Due to the limitations of simulation tools, conventional evaluation approaches are limited to the circuit scale. The simulation runtime is unbearable for large-scale integrated circuits. This paper presents an approach for evaluating the soft error rate using machine learning. A back propagation neural network is implemented in the proposed approach. It helps to determine the probability of transient pulse propagation. Compared with the conventional soft-error-rate evaluation results, the proposed approach demonstrates a strong correlation in both trend and magnitude. The average difference between the results obtained using the proposed evaluation method and the experimental results is 23.5%, which is 7.5% higher than that between the results obtained using the conventional evaluation method and the experimental results. Compared to the conventional evaluation method, the proposed approach improves the runtime by an order of magnitude. The proposed approach also benefits the locating of highly sensitive circuit nodes in large-scale integrated circuits. Circuit design and radiation hardening are both useful applications.

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Section: Pulse Propagationmentioning

confidence: 99%

Section: The Ser Evaluation Approachmentioning

confidence: 99%