# Quantile Online Learning for Semiconductor Failure Analysis

arXiv: https://arxiv.org/abs/2303.07062 · HTML: https://mlsanity.com/paper/2303.07062/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [DEVDAN: Deep Evolving Denoising Autoencoder](https://mlsanity.com/paper/1910.04062/) (2019)
- [Data Normalization for Bilinear Structures in High-Frequency Financial Time-series](https://mlsanity.com/paper/2003.00598/) (2020)
- [Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments](https://mlsanity.com/paper/1810.07348/) (2018)
- [Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction](https://mlsanity.com/paper/2201.05459/) (2022)
- [Adaptive Normalization in Streaming Data](https://mlsanity.com/paper/1910.07696/) (2019)

## Built on by (most cited first, 1 of 1)

- [Incremental Multistep Forecasting of Battery Degradation Using Pseudo Targets](https://mlsanity.com/paper/2509.15740/) (2025)

## Abstract (arXiv)

With high device integration density and evolving sophisticated device structures in semiconductor chips, detecting defects becomes elusive and complex. Conventionally, machine learning (ML)-guided failure analysis is performed with offline batch mode training. However, the occurrence of new types of failures or changes in the data distribution demands retraining the model. During the manufacturing process, detecting defects in a single-pass online fashion is more challenging and favoured. This paper focuses on novel quantile online learning for semiconductor failure analysis. The proposed method is applied to semiconductor device-level defects: FinFET bridge defect, GAA-FET bridge defect, GAA-FET dislocation defect, and a public database: SECOM. From the obtained results, we observed that the proposed method is able to perform better than the existing methods. Our proposed method achieved an overall accuracy of 86.66% and compared with the second-best existing method it improves 15.50% on the GAA-FET dislocation defect dataset.