System for monitoring semiconductor process based on digital twin

A computing device comprising:

• at least one processor configured to execute computer-readable instructions included in memory,
• wherein the at least one processor is configured to:
• process a process of simulating a semiconductor process on a digital twin-based fabrication (FAB) through a data-driven digital twin model consisting of a cyber-physical system (CPS) model that derives same simulation results in a virtual environment based on a past data history of a physical system;
• predict a semiconductor die or a device under test (DUT) in which a failure or fault of a facility is expected during a process, perform virtual metrology in each process step through at least one prediction model, among support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF) prediction models, based on sensor data collected in a manufacturing facility and test measuring data, and predict whether a chip that has passed through a reconditioning test (RT) passes through other reliability tests in advance, wherein the data collected in the manufacturing facility consist of complex data, such as a sensor signal, an image, or video, and a state of the data is tracked based on the past facility error history data or data during a normal operation interval;
• share simulation results between digital twin models for individual equipment, and perform end-to-end (E2E) simulations by associating simulation results of one digital twin model with another digital twin model as different digital twin models are coupled in parallel, wherein the digital twin model is applied to the entire semiconductor process based on the sensor data of each of pieces of equipment and the image data of a wafer;
• utilize an electrical die sorting (EDS) yield prediction model for reducing costs of the semiconductor process, and detect a defective wafer by using at least one of sensor data, sound measuring data, an image, or video in a post-process step comprising chipping detection and prediction through a determination of a change in a dicing signal;
• manage a database in which the sensor data of a facility to be learnt and the simulation results of the digital twin system are retained and exchanged in order to predict the product of each digital twin model;
• interact with a digital twin system related to the semiconductor process, a server, a learning model, and an individual facility through a hyper converged infrastructure (HCI) framework that constructs a service for an interaction with a worker;
• set a range in which data is collected, construct a database based on the collected data so that data generated in digital twin virtual metrology (VM) is stored, derive vital few through the analysis of a fault and cause factor correlation by using package tester equipment data, apply a convolution neural network (CNN)-based AutoEncoder model as an abnormal detection-early prediction model, and select a base model for the prediction of a fault in a semiconductor test process and perform a pre-test based on the base model, wherein domain knowledge is secured and a corresponding component is designed so that the system is decomposed for each step and reassembled into a cyber-physical system (CPS) in order to construct the digital twin system for the semiconductor process;
• operate a DT-control interface for exchanging simulation input/output data between digital twin VMs, wherein the digital twin model predicts a virtual product by applying a data-driven bidirectional LSTM (BLSTM) model so that the omission of data, non-linear distribution data, and a parameter loss are handled, and predicts the final product as the results of continuous simulations by tracking the input/output results of each CPS model through the digital twin;
• monitor a plurality of VMs through a DT-DT system in which the digital twin has been coupled in parallel so that a facility error occurrence pattern is estimated by using real process facility data;
• and
• provide a worker with notification so that the worker finds process equipment that is expected to be problematic through back tracking for individual process equipment when an outlier is detected in the results of FAB-operation simulations.