From the 1 of 9 linked papers with an AI index.
9 papers
Recursive transformers for semiconductor thermo-mechanical reliability
Kart-leong Lim
The paper evaluates three recursive transformer architectures that share weights to create efficient surrogate models for thermo‑mechanical reliability analysis of semiconductor pa…
PYPM-GGD: Pitman-Yor Process Mixture with Generalized Gaussian Density using ADAM
Kart-Leong Lim
Large scale Bayesian nonparametrics (BNP) learner such as Stochastic Variational Inference (SVI) can handle datasets with large class number and large training size at fractional c…
Inverse prediction of capacitor multiphysics dynamic parameters using deep generative model
Kart-Leong Lim, Rahul Dutta, Mihai Rotaru
Finite element simulations are run by package design engineers to model design structures. The process is irreversible meaning every minute structural adjustment requires a fresh i…
Physics Informed Neural Network using Finite Difference Method
Kart Leong Lim, Rahul Dutta, Mihai Rotaru
In recent engineering applications using deep learning, physics-informed neural network (PINN) is a new development as it can exploit the underlying physics of engineering systems.…
Prognostics and Health Management of Wafer Chemical-Mechanical Polishing System using Autoencoder
Kart-Leong Lim, Rahul Dutta
The Prognostics and Health Management Data Challenge (PHM) 2016 tracks the health state of components of a semiconductor wafer polishing process. The ultimate goal is to develop an…
Fisher Information based Stochastic Gradient Ascent for Online Learning of Dirichlet Process Mixture and Theory
Kart-Leong Lim, Xudong Jiang
Scalable algorithms of posterior approximation allow Bayesian nonparametrics such as Dirichlet process mixture to scale up to larger dataset at fractional cost. Recent algorithms,…