Performance of a manufacturing tool is optimized. Optimization relies on recipe drifting and generation of knowledge that capture relationships among product output metrics and input material measurement(s) and recipe parameters. Optimized recipe parameters are extracted from a basis of learned functions that predict output metrics for a current state of the manufacturing tool and measurements of input material(s). Drifting and learning are related and lead to dynamic optimization of tool performance, which enables optimized output from the manufacturing tool as the operation conditions of the tool changes. Features of recipe drifting and associated learning can be autonomously or externally configured through suitable user interfaces, which also can be drifted to optimize end-user interaction.

Method and apparatus for self-learning and self-improving a semiconductor manufacturing tool

https://pureadmin.qub.ac.uk/ws/files/136557002/fsiv_ieea_final_accepted_paper.pdf

An enhanced variable selection and Isolation Forest based methodology for anomaly detection with OES data

Modeling of manufacturing processes is important because it enables manufacturers to understand the process behavior and determine the optimum operating conditions of the process for a high yield, low cost and robust operation. However, existing techniques in modeling manufacturing processes cannot address the whole common issues in developing models for manufacturing processes: a) manufacturing processes are usually nonlinear in nature; b) a small amount of experimental data is only available for developing manufacturing process models; c) outliers often exist in experimental data; d) explicit models in a polynomial form are often preferred by manufacturing process engineers; and e) models with satisfactory prediction accuracy are required. In this paper, a modeling algorithm, namely, the particle swarm optimization-based fuzzy regression (PSO-FR) approach, is proposed to generate fuzzy nonlinear regression models, which seek to address all of the common issues in developing models for manufacturing processes. The PSO-FR first employs the operations of particle swarm optimization to generate the structures of the process models in nonlinear polynomial form, and then it employs a fuzzy coefficient generator to identify outliers in the original experimental data. Fuzzy coefficients of the process models are determined by the fuzzy coefficient generator in which the experimental data excluding the outliers is used. The effectiveness of the PSO-FR approach is evaluated by modeling the manufacturing process liquid epoxy molding process which is a commonly used technology for microchip encapsulation in electronic packaging. Results were compared with those based on the commonly used modeling methods. It was found that PSO-FR can achieve better goodness-of-fitness than other methods. Also, the prediction accuracy of the model developed based on the PSO-FR is better than the other methods.

Modeling of a Liquid Epoxy Molding Process Using a Particle Swarm Optimization-Based Fuzzy Regression Approach

Autonomous biologically based learning tool system(s) and method(s) that the tool system(s) employs for learning and analysis of performance degradation and mismatch are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. Objectively generated knowledge gleaned from synthetic or production data can be utilized to determine a mathematical relationship among a specific output variable and a set of associated influencing variables. The generated relationship facilitates assessment of performance degradation of a set of tools, and performance mismatch among tools therein.

Method and system for detection of tool performance degradation and mismatch

Techniques for measuring and/or compensating for process variations in a semiconductor manufacturing processes. Machine learning algorithms are used on extensive sets of input data, including upstream data, to organize and pre-process the input data, and to correlate the input data to specific features of interest. The correlations can then be used to make process adjustments. The techniques may be applied to any feature or step of the semiconductor manufacturing process, such as overlay, critical dimension, and yield prediction.

Process control techniques for semiconductor manufacturing processes

The objective of this paper is to present the utilization of information produced during plasma etching for the prediction of etch bias. A plasma etching process typically relies on the concentration of electrically activated chemical species in a reaction chambers over time, depending on chamber pressure, gas flow rate, power level, and other chamber and wafer properties. Plasma properties, as well as equipment factors, are complex and vary over time. In this paper, we will use various statistical techniques to address challenges due to the nature of plasma data: high dimensionality, colinearity, parameter interactions and nonlinearities, variation of data structure due to equipment condition changing over time, etc. The emphasis will be data integrity, variable selection, accommodation for process dynamics, and model-building methods. Different techniques will be evaluated with an industrial dataset.

Virtual Metrology Modeling for Plasma Etch Operations

A method to enable wafer result prediction includes collecting manufacturing data from various semiconductor manufacturing tools and metrology tools; choosing key parameters using an autokey method based on the manufacturing data; building a virtual metrology based on the key parameters; and predicting wafer results using the virtual metrology.

Novel Methodology To Realize Automatic Virtual Metrology

A system, method, and computer readable medium for extracting a key process parameter correlative to a selected device parameter are provided. In an embodiment, the key process parameter is determined using a gene map analysis. The gene map analysis includes grouping highly correlative process parameter and determining the correlation of a group to the selected device parameter. In an embodiment, the groups having greatest correlation to the selected device parameter are displayed in a correlation matrix and/or a gene map.

System for extraction of key process parameters from fault detection classification to enable wafer prediction

A method for monitoring a processing tool in a semiconductor manufacturing facility includes selecting key hardware parameters for a virtual sensor system based on manufacturing data associated with a fabrication tool and collecting manufacturing data associated with the fabrication tool. The method further includes dynamically maintaining the virtual sensor system during the manufacture of a plurality of semiconductor products and using the virtual sensor system and the collected manufacturing data for predicting a condition of a semiconductor product after being processed by the fabrication tool.

Chun-Hsien Lin

Papers

Virtual Metrology Modeling for Plasma Etch Operations

Novel Methodology To Realize Automatic Virtual Metrology

System for extraction of key process parameters from fault detection classification to enable wafer prediction

Method and apparatus to enable accurate wafer prediction

Extraction of key process parameter