A robust low data solution: dimension prediction of semiconductor nanorods

2020-10-27 · Xiaoli Liu, Yang Xu, Jiali Li, Xuanwei Ong, Salwa Ali Ibrahim, Tonio Buonassisi, Xiaonan Wang

Precise control over dimension of nanocrystals is critical to tune the properties for various applications. However, the traditional control through experimental optimization is slow, tedious and time consuming. Herein a robust deep neural network-based regression algorithm has been developed for precise prediction of length, width, and aspect ratios of semiconductor nanorods (NRs). Given there is limited experimental data available (28 samples), a Synthetic Minority Oversampling Technique for regression (SMOTE-REG) has been employed for the first time for data generation. Deep neural network is further applied to develop regression model which demonstrated the well performed prediction on both the original and generated data with a similar distribution. The prediction model is further validated with additional experimental data, showing accurate prediction results. Additionally, Local Interpretable Model-Agnostic Explanations (LIME) is used to interpret the weight for each variable, which corresponds to its importance towards the target dimension, which is approved to be well correlated well with experimental observations.

📄 PDF Abstract BibTeX arXiv:2010.14111

Code (0)

등록된 구현이 없습니다.

Tasks

Prediction regression

SemiConLens: Visual Analytics for 2D Semiconductor Discovery

2026-04-11 · Kavinda Athapaththu, Shiwei Chen, Yuan Fang, Sanchali Mitra 외 arxiv

The past few years have witnessed vibrant efforts in discovering new two-dimensional (2D) semiconductor materials from both academia and the industry, due to their promising potential in resolving the severe performance …

Unsupervised Anomaly Prediction with N-BEATS and Graph Neural Network in Multi-variate Semiconductor Process Time Series

2025-10-23 · Daniel Sorensen, Bappaditya Dey, Minjin Hwang, Sandip Halder arxiv

Semiconductor manufacturing is an extremely complex and precision-driven process, characterized by thousands of interdependent parameters collected across diverse tools and process steps. Multi-variate time-series analys…

Univariate Time Series Forecasting Graph Neural Network Anomaly Detection

Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays

2024-11-19 · Anantha S. Rao, Donovan Buterakos, Barnaby van Straaten, Valentin John 외

Arrays of gate-defined semiconductor quantum dots are among the leading candidates for building scalable quantum processors. High-fidelity initialization, control, and readout of spin qubit registers require exquisite an…

Exploring Active Learning for Semiconductor Defect Segmentation

2025-07-23 · Lile Cai, Ramanpreet Singh Pahwa, Xun Xu, Jie Wang 외 arxiv

The development of X-Ray microscopy (XRM) technology has enabled non-destructive inspection of semiconductor structures for defect identification. Deep learning is widely used as the state-of-the-art approach to perform …

Semantic Segmentation Active Learning

Semiconductor Industry Trend Prediction with Event Intervention Based on LSTM Model in Sentiment-Enhanced Time Series Data

2025-11-19 · Wei-hsiang Yen, Lyn Chao-ling Chen arxiv

The innovation of the study is that the deep learning method and sentiment analysis are integrated in traditional business model analysis and forecasting, and the research subject is TSMC for industry trend prediction of…

Sentiment Analysis