This blog was written by Ailee Grumbine- Keysight Memory Solutions Product Manager
As a design engineer, your job is to design the best product. Your manager’s job is to reduce the number of redesigns and deal with engineering shortages and budget constraints. Your manager asked for test results to decide if your product is ready for release to production. You would spend days analyzing the test results to gain confidence that your product is good. You then translate the information into graphs and test reports that are presentable to your manager. Does this all sound familiar?
Data analytics is the answer for overcoming these challenges. In the test and measurement industry, designers use test equipment to help determine if their design meets the industry passing criteria for device certifications. Data sources include test results from compliance test software, simulation software, multiple vendors test equipment, and individual company’s proprietary measurement tools. Data collected is exported to a data repository server or cloud which is accessible by a globally distributed design team. Data analytics with visualization tools helps the decision making process more intuitive and a lot faster. The visualization tools include line and histogram charts with pass fail limits and statistical information. The image below shows an example of a measurement jitter histogram plot of different ASIC names. It reveals that the two ASICs, SERDES 700 and SERDES 701. Both have the same histogram mode and profile while SERDES 702 doesn’t have enough measurement to conclude its performance. You may want to hold off SERDES 702 for release to production.
Histogram plot of jitter measurement on three different SERDES
The next example is a bit error measurement against input voltage for different ASIC versions. Alpha, beta, and gamma versions have the same bit error measurements, while delta version is performing better with lower bit error measurement. You could conclude that delta version ASIC has better performance compared to the other versions. It could also be that there is discrepancy in the way the measurement is made that causes the outlier behavior. You should also look at other possible contributing factors such as test equipment, test bench, and the engineer who made the measurement.
Line plot of bit errors on four different ASIC versions
The visualization tool is the easy part of setting up data analytics capability. The hard part is setting up a web server that would interact with the data repository server for data upload and access. The data repository server has to be secured and has the support for backup, restore, and replication. It is highly recommended to have company’s internal IT department support in setting up the data repository server. The web server hosts the data analytics web server application software. It needs to support massive data upload via streaming or bulk transfer. It needs to be OS and programming language independent. It has to protect the data from any corruption and ensures consistency. It is recommended that the web server and the data repository server is setup using two separate servers to allow for scalability, performance, and data repository security. You can collect the data in a .CSV file with measurements and properties information. Example of properties are temperature, test bench names, ASIC names, ASIC versions, and test engineers. Measurements can be jitter, bit error, input voltage, and power. For most measurements, there are upper and lower limits which would tell the design engineer the margins they have in their design.
Being ahead of the competition and doing it in the most cost efficient manner have a positive business impact. Hence, data analytics features are designed to work with all measurement data collection methods to allow for simple, quick, non-tedious integration into the design and characterization work flow. Important data analytics software features would include a web server application to enable real time huge data import and access. It would also support visualization tools with different chart options to enable fast and intuitive data analysis for making quick decisions. All of these elements should build an infrastructure that would support data analytics successfully in your company.