Machine learning is the ability of a machine to improve its performance based on its past performance, to then be used for prediction or classification purposes. It has grown out of the disciplines of statistics, Artificial Intelligence (AI) and neuroscience.

Statistics tended to focus on problems such as the limit convergence of estimators until Brieman, Friedman, Olshen and Stone, and later Hastie and Tibshirani.

**CLASSIFICATION AND REGRESSION TREES (CART)**

Beginning with Classification And Regression Trees (CART), these pioneers took a more serious approach to machine learning and applied it to real data. AI became stalled in rule-based expert systems until Judea Pearl helped move the field into probabilistic graphical models where classification, regression, dynamics, diagnostics, causal systems and decision theory can be wrapped into a powerful probabilistic framework.

Neuroscience helped bring about effective non-linear classification, regression and reinforcement learning in the form of neural networks and adaptive filters. Under Professor Michael Jordan, it has focused on the more theoretically founded kernel estimators, such as support vector machines, and is beginning to merge these with probabilistic graphical models.

This has produced mature tools that will return multiples of the time and money spent learning them: binary decision trees (CART, C4.5) and their statistically boosted variants (AdaBoost, MART, random forests); discrete graphical models in the form of diagnostic networks and influence diagrams; and support vector machines for prediction and feature selection.

With the explosion in internal and external networks, which allow the pooling of data, advantages such as cost savings, sophisticated trouble-shooting and automatic diagnostics are available with even the simplest applications. The main difficulties lie in collecting and organising the data that these routines need, and diffusing knowledge of them throughout the organisation.

Manufacturing management must create a top-down push for end-to-end use of machine learning and allow a bottom-up initiative to find specific applications. Otherwise, the organisation will be subject to untapped losses, unpredicted equipment failures and slower diagnostics.

**TECHNIQUES**

Comprehension of what follows is based on the assumption that a set of data has been developed consisting of either a label (good, bad, indifferent) or a regression target, and a set of feature variables. If dealing with cars, for instance, the label might be ‘price’ with features such as colour, weight, manufacturer or type. By using a classifier, the label that derives from each feature can be learnt and predicted. If there is a label, it is known as ‘supervised learning’. If there is no label, the task might be to cluster the data into like groups. Alternatively, one of the features can be selected as a label and the remaining features used to learn and predict that label. Typically, there is a ‘training set’ of data that can be used to learn the classifier responses or establish the categories. A ‘test set’ helps determine how well the data can be classified.

**IMPORTANCE VARIABLES**

One technique, described in the context of random forests but universal to any classifier, determines which features are important for prediction accuracy. After training the classifier, each feature is taken in turn and randomly permuted. The data is fed back in to see how bad the predictive performance is. The worse the permuted feature prediction results are, the more important that feature is to the classifier. In this way, important process parameters relating to success or failure are found.

**TREES AND NETWORKS**

Learning binary decision trees is about finding a feature variable and a value that best splits the data into two label groups until the data is pure, or nearly so, at ‘leaf’ level. For example, to divide cars into a high-mileage and low-mileage group, he best split variable might be weight (with two tonnes and over being the cut-off point). So, the cut-off point is divided down the tree, reusing feature variables as necessary, until the data in the ‘leaves’ is pure, or within a small percentage of being so. Stopping at a split level that is too high in the tree will lead to ‘underfitting’. Splitting to almost perfect purity at the bottom leads to ‘overfitting’. To achieve a true fit, CART first splits all the way down the tree, then prunes the tree back up using a tree complexity cost process.

Using this technique, each feature in a data set can be predicted using all the remaining features. Each feature yields a tree, and in each tree the top level splits and can be used to define a dependency network that shows which features predict others. Amazon.com uses a similar technique to predict from items their customers have bought or looked at to buy in the future. In semiconductors, manufacturers could use the method to find and visualise the dependency structure for items that failed or excelled.

Decision trees and dependency networks have the greatest range of usability, since trees handle mixed data types, as well as missing and hidden variables. Good manufacturing applications might be: predicting machine failure and which components will turn out well (and can undergo reduced testing); finding multiple causes of failure; and diagnosing causes of failure.

**STATISTICAL BOOSTING**

Boosting is a method of combining many weak classifiers into a strong classifier. Typically, the weak classifier is a ‘stump’ – a tree with only one split decision. Many stumps can be combined into one classifier as in the following AdaBoost training pseudo-algorithm:

1. Give each data point an equal weight

2. For a given number of iterations or until an error minimum is reached:

- Train a weak classifier with regard to the weighted data set
- Calculate the resulting weighted training error and use this to reweight the data and calculate a weighted vote of this classifier

3. Break if iterations or global error minimum are met, or else go back to 2

Between 100 and 500 weak classifiers are usually formed. When presented with a new test point, all classifiers will classify it and vote with their given weight.

**ALTERNATIVES TO BOOSTING**

There are similar techniques to boosting, though motivated by a different statistical root: the Law of Large Numbers. This law says that the average of many samples from the same class of variables will converge to the mean of that class. Stochastic discrimination uses this to build a strong classifier out of many weak classifiers, where the strong classifier will eventually converge to the mean of classes it attempts to predict. The weak classifiers are assigned at random and checked to see if they conform to:

- Encouragement (they predict better than 50%)
- Thickness (they generalise to new examples in the class)
- Non-special (adjustments to equalise coverage of easy and hard-to-classify points)

A similar technique, which is an example of stochastic discrimination but was invented separately by Leo Breiman, is ‘random forests’. The random forests technique builds many trees, each built down to perfect purity by splitting on a random subset of features at each split point. The number of features considered for splitting at any given branch point is the square root of the total number of features. In test mode, each tree classifies a new test data point.

The trees then get an equal vote on the point’s class. There are between 50 and 500 trees.

Stochastic discrimination, random forests and boosting tend to form more accurate classifiers than decision trees alone, since they are able to better capture the true structure of the data. Using the importance variable method, these classifiers can be used in place of single trees and still find important dependencies in the data.

**PROBABILISTIC GRAPHICAL MODELS**

Probabilistic graphical models can express either grids of locally correlated variables (Markov random fields) or directed causal relations between variables (Bayesian networks / directed graphical models), or mixes of the two (chain graphs).

This is a powerful formalism, subsuming such prior technologies as the Kalman Filter for tracking, Hidden Markov Models for speech recognition, independent components analysis, and turbo codes for communication error correction. Directed probability models, or Bayesian networks, can be viewed as either representing the causal relations between variables or expressing the conditional independence of variables. Conditional independence allows a probability distribution yielding large memory and computational savings to be factored into the resulting model.

Bayesian networks may also be seen as circuit diagrams of probability models. Statistics can only express correlation (for example, rain and wet grass occur together) while Bayesian networks express the causality (rain > wet grass). Note that in Bayesian networks causality is directed but signals flow both ways (wet grass means that it is more likely that rain occurred).

Relationships between probability models drive the network. Bayesian networks use conditional independence to factor a distribution, resulting in large memory and computational savings. As events are observed, one may infer the new probabilities by applying Bayes’ Rule as defined by Pearl’s Belief Propagation algorithm. The software cited offers many ways of solving, approximating and learning Bayesian networks.

The potential applications of Bayesian networks in semiconductor manufacturing are vast, but greatest in the introduction of cost-based decision-making influence diagrams. Bayesian networks use conditional independence to factor a distribution, resulting in large memory and computational savings. More advanced cost-based decision algorithms are called partially observable Markov decision processes. Another important area is diagnostics systems for machine monitoring and rapid repair. Bayesian networks use conditional independence to factor a distribution, resulting in large memory and computational savings.

**SUPPORT VECTOR MACHINES**

The basic idea behind Support Vector Machines (SVMs) is that if the data is projected in a sufficiently high dimensional space, the data becomes linearly separable. For example, if there is a two-bit exclusive OR (XOR) where x,y = [0,1] and the dataset is labelled as XOR(x,y):(x,y), this is not linearly separable. If a new dimension is added, XOR(x,y):(x,y,x*y), then the data becomes linearly separable. SVMs typically design a kernel function to map the data to a high dimensional space and then use optimisation routines to find a hyper-plane that best separates the data.

SVMs can be powerful prediction and classification machines. Intel Research is obtaining good results predicting the health of lithography machines from power-up test wafers. Spatial patterns map well to spatial kernels in SVMs.

**WHITHER MACHINE LEARNING?**

Often just applying a decision tree and thinking clearly about the results will generate 80% of the savings. Applications range from predicting which components are good (and need less testing) to predicting machine failure and forecasting market demand.

All it takes is a commitment to making manufacturing data easily available within the organisation, and a commitment to designing or buying the machine learning tools needed to play with the data. The ideal is top-down management harvesting of bottom-up results; after all, this is a profit centre. The payoffs will easily exceed the effort put in.