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期刊

  1. Pengwei Liu; Ren, X; Wang, P; Yuan, H; Hao, Z; Chen, G; Xu, C; Cai, S; Ni, D∗;# An Efficient Graph-Transformer Operator for Learning Physical Dynamics with Manifolds Embedding, Communications AI & Computing, 2026, accepted
  2. Jianming Guo; Fu, L; Geng, M; Bai, H; Xu, Q; Gao, D; Qi, D; Shi, X; Ni, D*;# High-Fidelity Multilayer Etching in Virtual Fabrication via Offline Reinforcement Learning, Materials Science in Semiconductor Processing, 2026, 209: 110640
  3. Qipei Zhang; Liu, P; Fang, W; Ni, D; Kong, Y*;# MPNet: A Modular Deep Learning Process TCAD Surrogate Modeling Framework, Journal of Semiconductors, 2026, 47(6), 062302
  4. Jianming Guo; Mou, Z; Ren, K; Gao, D; Ni, D*;# A Transformer-based Sequence Prediction Architecture for Etch Profile Evolution in Semiconductor Manufacturing, IEEE Transactions on Semiconductor Manufacturing, 2026, 39(2):248-255
  5. Jianming Guo; Mou, Z; Ren, K; Ni, D; Gao, D*;# Sequence Modeling for Predicting Three Dimensional Plasma Etching Profiles with Deep Learning, Journal of Vacuum Science & Technology A, 2025, 43 (4): 043005
  6. Fuxun Chen; Chen, Z; Zheng, C; Ni,D; Gao, D; Zhou, G*;# Achieving high-accuracy and noise-robust process window analysis through stepwise regression, Journal of Micro/Nanopatterning, Materials, and Metrology, 2025, 24(3): 034801
  7. Mingqiang Geng; Guo, J; Sun, Y; Gao, D*; Ni, D*;# Accurate and Efficient Process Modeling and Inverse Optimization for Trench Metal Oxide Semiconductor Field Effect Transistors: A Machine Learning Proxy Approach, Processes, 2025, 13(5), 1544
  8. Jianming Guo; Ren, K; Ni, D; Gao, D*;# Attention-enhanced Conditional Variational Autoencoder Integrating 3D Plasma Etching Simulation for Etching Process Optimization, Journal of Vacuum Science & Technology A, 2025, 43: 023003
  9. Pengwei Liu; Ni, D*;# A Deep-learning-based Proxy Modeling Method with Application to Plasma Processing, Chemical Engineering Research and Design, 2024, 211: 299-317
  10. Yian Wang; Wu, Z; Ni, D*;# Large-Scale Optimization among Photovoltaic and Concentrated Solar Power Systems: A State-of-the-Art Review and Algorithm Analysis, Energies, 2024, 17:4323
  11. Jianming Guo; Geng, M; Ren, K*; Ni, D*; Gao, D*;# Optimizing Plasma Etching: Integrating Precise Three-Dimensional Etching Simulation and Machine Learning for Multi-Objective Optimization, IEEE Access, 2024, 12:127065-127073
  12. Yuting Kong; Ni, D*;# Correlation Analysis of Sampled Wafer Profile Maps Based on a Deep Reconstruction Model, Applied Soft Computing, 2024, 159:111634,
  13. Yi Wang; Ni, D*; Huang, Z; Chen, P;#A self-supervised representation learning method for complex wafer bin map classification, Expert Systems with Applications, 2024, 249:123601
  14. Yujun Liu; Ni, D*; Wang, Z;# A fault-tolerant soft sensor algorithm based on long short-term memory network for uneven batch process, Processes, 2024, 12(3), 495
  15. Yian Wang; Wu, Z; Ni, D*;# Real-Time Optimization of Heliostat Field Aiming Strategy via an Improved Swarm Intelligence Algorithm, Applied Sciences, 2024, 14(1), 416
  16. Sipei Wu; Ni, D*;# A Method for Real-time Optimal Heliostat Aiming Strategy Generation via Deep Learning, Engineering Applications of Artificial Intelligence, 2024, 127:107279
  17. Yujun Liu; Ni, D*; Shao, X; Gong, D; Li, J;# A Hierarchical Model-based method for wafer level virtual metrology under process information deficiency, Quality Engineering, 2023, DOI: 10.1080/08982112.2023.2252891
  18. Yi Wang; Ni, D*; Huang, Z*;# A momentum contrastive learning framework for low-data wafer defect classification in semiconductor manufacturing, Applied Sciences, 2023, 13(10), 5894
  19. Yi Wang; Ni, D*;# A Deep Learning Analysis Framework for Complex Wafer Bin Map Classification, IEEE Transactions on Semiconductor Manufacturing, 2023, 36(3): 367-377
  20. Gang Xiao; Wang, Z; Ni,D; Zhu,P*;# Kinetics and structural optimization of cobalt oxide honeycomb structures based on thermochemical heat storage, Energies, 2023, 16(7): 3237
  21. Ruidi Zhu; Ni, D*;# A Model Predictive Control Approach for Heliostat Field Power Regulatory Aiming Strategy under Varying Cloud Shadowing Conditions, Energies, 2023, 16(7): 2997
  22. Lingfeng Liu; Ni, D*; Dai, L;# Spatial Anomaly Detection in Hyperspectral Imaging using Optical Neural Networks, IEEE Intelligent Systems, 2023, 38(2): 64-72
  23. Gang Xiao; Yang, J; Ni, D*;# Model Predictive Control of a Solar Power System with Micro Turbine and Thermochemical Energy Storage, Industrial & Engineering Chemistry Research, 2022, 61, 13532-13558
  24. Tianqi Xiao; Wu, Z; Christofides, PD; Armaou, A; Ni, D*;# Recurrent neural network based model predictive control of a plasma etch process, Industrial & Engineering Chemistry Research, 2022, 61(1): 638–652
  25. Ruidi Zhu; Ni, D*; Yang, T; Yang, J; Chen, J; Xiao, G;# Heliostat Field Aiming Strategy Optimization with Post-installation Calibration, Applied Thermal Engineering, 2022, 202: 117720
  26. Yuting Kong; Ni, D*;# A One-shot Learning Approach for Similarity Retrieval of Wafer Bin Maps with Unknown Failure Pattern, IEEE Transactions on Semiconductor Manufacturing, 2022, 35(1): 40-49
  27. Zhichen Zeng; Ni, D*; Xiao, G;# Real-time heliostat field aiming strategy optimization based on reinforcement learning, Applied Energy, 2022, 307: 118224
  28. Jiayang Ren; Ni, D*;# A Real-time Monitoring Framework for Wafer Fabrication Processes with Run-to-Run Variations, IEEE Transactions on Semiconductor Manufacturing, 2021, 34(4): 483-492
  29. Jiamin Yang; Xiao, G*; Ghavami, M; Al-Zaili, J; Yang, T; Sayma, A; Ni, D;# Thermodynamic modelling and real-time control strategies of solar micro gas turbine system with thermochemical energy storage, Journal of Cleaner Production, 2021, 304:127010
  30. Tianqi Xiao; Ni, D*;# Multiscale Modeling and Recurrent Neural Network Based Optimization of a Plasma Etch Process, Processes, 2021, 9(1), 151
  31. Yuting Kong; Ni, D*;# Qualitative and Quantitative Analysis of Multi-Pattern Wafer Bin Maps, IEEE Transactions on Semiconductor Manufacturing, 2020, 33(4): 578-586
  32. Jiayang Ren; Ni, D*;# A Batch-Wise LSTM-Encoder Decoder Network for Batch Process Monitoring, Chemical Engineering Research and Design, 2020, 164: 102-112
  33. Tianqi Xiao; Ni, D*;# Multiscale Modeling and Neural Network Model Based Control of a Plasma Etch Process, Chemical Engineering Research and Design, 2020, 164: 113-124
  34. Yuting Kong; Ni, D*;# A Semi-Supervised and Incremental Modeling Framework for Wafer Map Classification, IEEE Transactions on Semiconductor Manufacturing, 2020, 33(1): 62-71
  35. Junyao Xie; Xu, Q; Ni, D; Dubljevic, S*;# Observer and Filter Design for Linear Transport-Reaction Systems, European Journal of Control, 2019, 49: 26-43
  36. Guilherme Ozorio Cassol; Ni, D; Dubljevic, S*;# Heat Exchanger System Boundary Regulation, AIChE Journal, 2019, 65(8): e16623
  37. Yan-Yan Zhang; Cheng, R; Ni, D; Tian, M; Lu, JW; Zhao, Y*;# Thermal conductivity characterization of ultra-thin silicon film using the ultra-fast transient hot strip method, Chinese Physics B, 2019, 28(7): 078105
  38. Gang Xiao; Zheng, G; Ni, D; Li, Q; Qiu, M; Ni, M*;# Thermodynamic assessment of solar photon-enhanced thermionic conversion, Applied Energy, 2018, 223: 134-145
  39. Mingjiang Ni; Yang, T; Xiao, G*; Ni, D; Zhou, X; Liu, H; Sultan, U; Chen, J; Luo, Z; Cen, K;# Thermodynamic analysis of a gas turbine cycle combined with fuel reforming for solar thermal power generation, Energy, 2017, 137: 20-30
  40. Wu, Zhe; Aguirre, A; Tran, A; Durand, H; Ni, D; Christofides, PD*;# Model Predictive Control of a Steam Methane Reforming Reactor Described by a Computational Fluid Dynamics Model, Industrial & Engineering Chemistry Research, 2017, 56(20):6002-6011
  41. Gang, Xiao; Yang, T; Liu, H; Ni, D; Ferrari, ML; Li, M; Luo, Z; Cen, K; Ni, M*;# Recuperators for micro gas turbines: A review, Applied Energy, 2017, 197: 83-99
  42. Gang, Xiao; Yang, T; Ni, D*; Cen, K; Ni, M;# A model-based approach for optical performance assessment and optimization of a solar dish, Renewable Energy, 2017, 100: 103-113
  43. Kwon, Joseph Sang-Il; Nayhouse, M; Orkoulas, G; Ni, D*; Christofides, PD*;# A method for handling batch-to-batch parametric drift using moving horizon estimation: Application to run-to-run MPC of batch crystallization, Chemical Engineering Science, 2015, 127:210-219
  44. Crose, Marquis; Kwon, JS; Nayhouse, M; Ni, D; Christofides, PD*;# Multiscale modeling and operation of PECVD of thin film solar cells, Chemical Engineering Science, 2015, 136:50-61
  45. Kwon, Joseph Sang-Il; Nayhouse, M; Orkoulas, G; Ni, D*; Christofides, PD*;# Run-to-run-based model predictive control of protein crystal shape in batch crystallization, Industrial & Engineering Chemistry Research, 2015, 54(16):4293-4302
  46. Ni, Dong; Christofides, PD*;# Multivariable predictive control of thin film deposition using a stochastic PDE model, Industrial & Engineering Chemistry Research, 2005, 44(8):2416-2427
  47. Ni, Dong; Christofides, PD*;# Dynamics and control of thin film surface microstructure in a complex deposition process, Chemical Engineering Science, 2005, 60(6):1603-1617
  48. Ni, Dong; Lou, YM; Christofides, PD*; Sha, L; Lao, S; Chang, JP;# Real-time carbon content control for PECVD ZrO2 thin-film growth, IEEE Transactions on Semiconductor Manufacturing, 2004, 17(2):221-230

顶会

人工智能顶会

  1. Pengkai Wang; Liu, P; Zuo, Qi; Sang, Z; Xie, C; Ni, D*; Yang, H*;# InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training, Proceedings of the 43st International Conference on Machine Learning (ICML), 2026
  2. Ren, Xingyu; Liu, P; Wang, P; Chen, G; Wu, Q; Ni, D*;# Uncertainty-Informed Meta Pseudo Labeling for Surrogate Modeling with Limited Labeled Data, Advances in Neural Information Processing Systems (NeurIPS), 2025
  3. Chen, Shuhang; Yuan, H; Liu, P; Gu, H; Feng, T; Ni, D*;#SAMora: Enhancing SAM through Hierarchical Self-Supervised Pre-Training for Medical Images, Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2025
  4. Liu, Pengwei; Wang, P; Ren, X; Yuan, H; Hao, Z; Xu, C; Cai, S; Ni, D*;# AeroGTO: An Efficient Graph-Transformer Operator for Learning Large-Scale Aerodynamics of 3D Vehicle Geometries, Proceedings of the AAAI Conference on Artificial Intelligence, 2025
  5. Liu, Pengwei; Ni, D*;# PAPM: A Physics-aware Proxy Model for Process Systems, Proceedings of the 41st International Conference on Machine Learning (ICML), 2024
  6. Yuan, Hangjie; Zhang, S; Wang, X; Wei, Y; Feng, T; Pan, Y; Zhang, Y; Liu, Z ; Albanie, S; Ni,D*;# InstructVideo: Instructing Video Diffusion Models with Human Feedback, The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
  7. Liu, Lingfeng; Ni, D*;# LUM-ViT: Learnable Under-sampling Mask Vision Transformer for Bandwidth Limited Optical Signal Acquisition, International Conference on Learning Representations (ICLR), 2024
  8. Yuan, Hangjie; Zhang, S; Wang, X; Albanie, S; Pan, Y; Feng, T; Jiang, J; Ni, D*; Zhang, Y; Zhao, D;# RLIPv2: Fast Scaling of Relational Language-Image Pre-training, Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2023
  9. Yuan, Hangjie; Jiang, J; Albanie, S; Feng, T; Huang, Z; Tang, M; Ni, D*;# RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection, Advances in Neural Information Processing Systems (NeurIPS 2023), 2022 (Spotlight Paper)
  10. Yuan, Hangjie; Ni, D*;# Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics, Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(3), 3206-3214
  11. Yuan, Hangjie; Ni, D*;# Spatio-Temporal Dynamic Inference Network for Group Activity Recognition, Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021, 7476-7485
  12. Yuan, Hangjie; Ni, D*;# Learning Visual Context for Group Activity Recognition, Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(4), 3261-3269

光热发电顶会

  1. Wu, Sipei; Ni, D*;# Real-time Heliostat Field Aiming Strategy Generation for Varying Cloud Shadowing using Deep Learning, AIP Conference Proceedings 2303 (SolarPACES 2021), 2022, United States
  2. Zeng, Zhichen; Ni, D*;# Harvesting Optimal Operation Strategies from Historical Data for Solar Thermal Power Plants using Reinforcement Learning, Proceedings of the 26th SolarPACES International Conference (SolarPACES 2020), Online, United States
  3. Zhu, Ruidi; Ni, D*; Yang, T; Yang, J; Chen, J; Xiao, G;# Post-Installation Heliostat Field Aiming Strategy Optimization Based on Heliostat Flux Distribution Measurements, Proceedings of the 25th SolarPACES International Conference (SolarPACES 2019), Daegu, South Korea

专利

发明专利：

  1. 一种碟式镜面三维结构实时监控的方法，ZL 2018 1 1115228.X，倪东、易振彧，2018
  2. 基于光谱成像的等离子体组分空间分布实时测量方法及其装置，ZL 2018 1 0858346.3，倪东、朱锋、任佳阳，2018
  3. 基于毫米波雷达的跟随搬运车及其控制方法，ZL 2018 1 0982 102.6，倪东、朱锋，2018
  4. 一种基于半监督学习的晶圆图批量分析方法，ZL 2018 1 0792 257.3，倪东、孔煜婷，2018
  5. 一种超大规模晶圆缺陷数据的特征提取方法，ZL 2020 1 0955 780.0，倪东、王皓玥，2020
  6. 一种基于多旋翼无人机的塔式光热电站镜场巡检方法及装置，ZL 2022 1 0003 230.8，倪东、沈唯鑫，2023
  7. 一种基于毫米波雷达的多目标跟踪与呼吸检测方法与装置，ZL 2021 1 1612 665.4，倪东、李哲远，2025
  8. 一种利用超声波优化光刻胶均匀性的涂胶方法，ZL 2024 1 0952094.6，郑超怡;周国栋;黄烈杰;黄天昊;杨佳斐;倪东;高大为
  9. 基于逐步回归的光刻工艺窗口分析方法、装置和设备，ZL 2024 1 1422657.7，陈甫讯;周国栋;陈泽阳;郑超怡;倪东;高大为
  10. 面向多缺陷图案晶圆图的数据增强和良率分析方法、系统，ZL 2025 1 1292862.0，孔煜婷；谢俊杰；倪东；李子亮
  11. 一种基于发射光谱的低温等离子体浓度软测量方法及装置，ZL 2022 1 1076605.X，倪东、顾子超，2022

软件著作权：

  1. 工业解析与控制有限元软件【简称：IACFEM】，2018SR1036294，刘力铨、倪东，2018
  2. 高温热化学储热系统辅助设计软件，2019SR1383228，郝秋霞、倪东，2019
  3. 超大规模晶圆缺陷数据快速分析软件，2020SR1647845，王皓玥、倪东，2020
  4. 基于实时光线追踪的聚光镜场仿真平台，2023SR0473533，倪东、苏裕宁，2023

荣誉

  • 2019 – 孔煜婷，IEEE SMILE 2019会议Excellent Student Paper Award
  • 2018 – 倪东，入选浙江省湖州市“南太湖精英计划”（创业长期）
  • 2014 – 倪东，入选浙江省“千人计划”（创新长期）
  • 2014 – 倪东，入选浙江大学首批“百人计划”
  • 2013 – 倪东，入选中组部“千人计划”（青年项目）
  • 2010 – 倪东，Intel China Award
  • 2004 – 倪东，美国自动控制委员会（AACC）年度最佳论文奖（O. Hugo Schuck Best Paper Award）