Data Visualization

Can Metrology Data Enhance Semiconductor Process Yield?

The demand for semiconductor chips has drastically increased due to digital transformation. Semiconductor manufacturing companies are struggling to keep up with the demand. They are also looking for innovative methods to upscale production and enhance the end-user experience. Incorporating metrology data has become one of the most reliable and efficient tools to increase semiconductor production. It has improved the profit margins and increased production yields from existing resources and processes. In his article for Medium, Brian Mattis shares how metrology data bolsters semiconductor process yield.

Metrology Data and Its Effect on Semiconductor Productivity

Most semiconductor manufacturing processes work under the statistical process control (SPC) system. The deposition of mechanical information can make it difficult for the technological framework to upscale productivity. Using metrology data and aligning it with machine learning tools can help you identify manufacturing issues. For instance, the turn-on voltage could be higher than required or the junction capacitance is low. Metrology data implementation will lessen the possibility of such errors.

Data Science Components You Should Incorporate

Data science allows you to manage several business variables simultaneously. Here is a list of variables that you should pay attention to:

  1. Inline metrology
  2. End-of-line electrical test
  3. Tool process qualification procedures
  4. Product circuit productivity

Tips to Enhance Semiconductor Process Yield

Many issues are somewhat connected on a wafer level in the semiconductor manufacturing process. There are distinct variables that you should acknowledge and evaluate. Issues like sudden termination of a wafer process or dysfunctional segments of wafers can be resolved. Metrology data preparation can help you prepare a list of end-of-line to subscribe test data that also focuses on tool qualification. For better data implementation, here is a list of steps that you should follow:

  1. Translate coordinates to Cartesian coordinates.
  2. Enrich the data with information like processing time.
  3. Filter down the data with the closest processing date and find the measurement that is closest to the product die coordinates.

Metrology Data Modeling

The goal of this step is not to implement the highest accuracy model. The focus is on accurately predicting data yield that the data engineer can easily understand. There are several algorithms that you can explore. Once you finalize the algorithm tools, start working on the data visualization process.

How to Enhance Predictive Yield Power

Predictive yield power can be understood as the ability of data structure to predict wafer yield before it finishes processing. It can help stakeholders make crucial decisions, such as:

  1. Initiating a rework process
  2. Providing a milestone report
  3. Cutting costs and unnecessary lots
  4. Replacing low-productivity yield to reduce delay

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