Victory Analytics
Data Analysis Tool by Machine Learning
Overview

Victory Analytics is a machine learning-based analytics tool created especially for the technology industry’s research,development, and manufacturing. Data management and visualization, feature selection and training, modeling, andoptimization components make up this system. The adaptability of the software tool enables it to cover both module level
in-depth data analysis such as electrical device characterization, process flow optimization, and fab tool recipe optimizationand factory level yield improvement such as Fault Detection & Classification (FDC) and Statistical Process Control (SPC).

Key Features• Multivariate data analysis using a combination ofadvanced statistics, neural networks, and decision
trees algorithm.
• Capabilities for managing input data, such asdetecting outliers and missing data imputation• Support multi-level classification analysis driven by
the classical statistics and machine learning algorism• Advanced Design of Experiments algorithm such as
computer-generated D-optimal DOE• State-of-the-art data filtering and feature selection
techniques based on the decision forest algorithm• Various sensitivity algorithms including Sobol, decision
forest, etc.
• Victory Analytics can conduct time series analysis
including AR/MA as well as seasonality analysis• An integrated visualization tool that offers data insight(Chart Designer)• Provide a python interface that enables complete
end-user automation• Stand-alone product on Windows and Linux OS

Applications for Victory Analytics• Manufacturing processes: Identify bottlenecks in
manufacturing processes, improve quality control, andreduce costs• Device/Circuit design and optimization: Engineers
make better decisions by providing them with insights
into their data. Increases the operation margin by theutilization of systematic data training• Product Yield improvement: Quality control of process
specification and/or target metrics. Data-driven
decision making

VictoryDoE Main View

Data Analysis and Modeling

Victory Analytics involves collecting, cleaning, and analyzing
data to identify trends, patterns, and relationships. It isan iterative process that involves a sequence of steps
and requires general understanding of your input andoutput variables. It is recommended to set up the correct
workflow:• Dataset generation: Generate dataset for modelingincluding identifying categorical variables, duplicatedinputs, etc. Transformation and subset of data ifnecessary• Regression term selection: Selection of predictors andregression terms for modeling• Model build: Build regular or M.L. regression model.
• Model analysis: Model score, sensitivity, and data
trend analysis

Multi-level Classification by Decision
Forest Algorithm

Victory Analytics provides multi-level classification inwhich the target variable has more than two categories.
The engine behind is the decision forest algorithm. It
produces multiple decision trees, each one trained on aslightly different subset of the data then take a majority
vote for classification. The figure below shows theschematic diagram of decision forest algorism and thefinal results showing 3-level classification.

Advanced Filtering and Feature Selection• The workspace is to manage simulation projects,
which are organized like directory structures• VictoryDoE supports multiple workspaces• At each project, users can assign a flag and attachcomments• All essential file management tools are provided: copy(clean), paste, archive, clean project, lock, etc.
• Project view allows users to do search and sort basedon the file name, time, flag, file size, etc.
• Shared workspace is useful in the collaborative
workflow in which multiple users share projects• Simulation examples are available through default
example workspace

Neural Network Parameters

Victory Analytics Neural Network is based on the supervised
learning configuration and Feed Forward Architecture.
Having a non-linear activation function in neurons does
makes a N.N. non-linear, which is the biggest advantage
of NN regression over conventional polynomial regression
method. The figure below is the schematic diagram of N.N.
configuration in Victory Analytics.

Time Series Analysis

Time series analysis is a method for studying data collected
at specific points in time and uncover patterns and trends
within that data, as well as forecasting future values.
Victory Analytics is based on ARMA (Autoregressive
Moving Average), a statistical method used for timeseries analysis and forecasting. It combines two simpler
models, AR (Autoregressive) and MA (Moving Average),to capture the influence of past values and random errors
on a future value in the time series.

Advanced Filtering: iForest and Actual vsPrediction MethodIsolation Forest is a state-of-the-art anomaly/outlier
detection algorithm based on decision tree techniquein Machine Learning. It was built because anomalies
are the data points that are “few and different”. Victory
Analytics provides iForest algorithm to effectively detect
the anomality.

In addition, there is a bottom-up approach in which themodel is completed and weed out the outlier from theActual-Predict graph. In this approach, users can selectthe outliers from the trend distribution and enhance thedata model.

In conjunction with optimization algorithm, Victory
Analytics provides Desirability analysis. It is a data analysis
technique that quantifies the multiple output target intoa single index. It is predicated on the assumption that
there is an ideal set of values for the data and that any
divergence from this ideal set of values is bad.

Feature Selection by Decision Forest
Algorithm

When building a random forest, each decision tree splits
the data based on features that predict the target variable.
By tracking how often a feature is used for splitting acrossall the trees in the forest, Victory Analytics reports how
important that feature is for the overall model.

Optimization and Desirability

Victory Analysis provides various optimization algorithms.
The choice of optimization options is attributed by various
conditions such as non-linearity of the model and numberof inputs and outputs. The optimization algorithms are:Local Optimization Algorithms• Levenberg-Marquardt• Hooke-Jeeves
Global Optimization Algorithms• Simulated Annealing• Parallel Tempering• Genetic Algorithm• Differential Evolution

Design of Experiments• Generate DOE table from various DOE algorithms• Option to add Augmented and Constraints DOEfeatures• Provides state-of-the-art DOE algorithms such as
computer-generated DOE from D-optimizationalgorithm• Visualization of DOE table• DOE builder supports Fraction of Design Space (FDS)plot• Power analysis of DOE is conducted in the DOE builder

Monte-Carlo Simulation for Cp/Cpk Analysis

Monte-Carlo simulation is performed based on the mean and sigma value ofthe input variables. The output distribution after more than 100k simulation
represents the potential distribution in the real world. From MC simulation
user can achieve• Cp/Cpk estimation• Fail rate against USL/LSL• Various distribution functions other than normal distribution are provided

Chart Designer

Chart Designer is a data visualization tool that offers insight into the dataset.
•		X-Y plot with with X-axis as a string value•		Bar chart•		Scatter plot
•		Box plot

(a)

(b)

(c)

(d)

(a) X-Y plot with displaying Y1 and Y2(b) Overlay of scatter and bar chart(c)		 Multiple X-Y plot based on the subset of the data(d) Box plot

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