Videos#
Browse Projects: From Device to Wafer-Level Analysis#
Step 1: Explore the Cell View#
Access the Cell View to explore all cells extracted from the GDS file or device manifest.
Each cell can include simulations or measurements, and attributes like XY coordinates, width, and length.
GDS Visualization (optional): If available, visualize the GDS file, which is particularly useful for collaboration between testers and designers.
Step 2: View Device Details#
Dive into device-level measurements and simulations for insights into performance.
Observe measurement variations across different dies and analyze die X and Y coordinates.
Die-Level Analysis#
Select individual dies (e.g., die 2,0 on a wafer) to view die-level analysis.
This helps in detecting performance variations across dies on the same wafer.
Wafer-Level Analysis#
Analyze sheet resistance distribution across the wafer and see pass/fail results based on spec limits.
Non-functional dies outside spec limits are highlighted, aiding quick problem identification.
Cross-Wafer Comparison#
Compare multiple wafers by creating comparison plots (e.g., sheet resistance distribution).
Set min/max values to filter outliers like shorts or open circuits.
Interactively display and analyze individual wafers’ distributions for greater detail.
GDS Visualization#
Use GDS file visualization to inspect chip layouts, compare designs, or update attributes.
This feature is valuable for continuous design iteration and detailed analysis.
Summary#
In this video, we covered navigating projects from cells to full-lot analysis. You can now visualize GDS files, analyze devices, compare wafers, and more. We hope this guide has been helpful, and look forward to seeing you next time.
Upload Measurement or Simulation Data#
Select Device:
Navigate to Cells and select the device you want to upload data for.
Click New Data and choose your data type (e.g., measurement).
Upload Data:
The tool supports both CSV and JSON formats for data uploads.
Set Details:
Set the time to now.
(Optional) Add details like Wafer ID, DieX, DieY, or other attributes you’d like to track.
For example, enter a temperature of 25°C, or any other relevant values.
Configure Plotting Settings:
Choose x_col for the x-axis.
Select one or more columns for the y-axis from your data.
Complete Upload:
Once everything is configured, click Upload.
Your data is now uploaded to the database, ready for visualization and analysis. You can view all entered attributes, zoom in to explore details, and multiple channels will be displayed if applicable.
Trigger an Analysis on Uploaded Data#
Select Measurement:
Go to Cells and select the measurement you uploaded.
Click New Analysis.
Choose Analysis Function:
In the Analysis tab, select from available analysis functions.
Customize parameters as needed (e.g., for a device power envelope analysis with 10 samples to create a window average and smooth data).
View Results:
Once the analysis is complete, view both the input parameters and output results.
You can download the results as a ZIP file containing JSON data and a PNG plot.
Run Comparison Analysis:
Trigger a second analysis with n = 2 samples to smooth data with fewer samples and compare with the first analysis.
Delete Analysis:
Click Delete to remove the analysis from the database if needed.
In this video, we covered triggering an analysis, viewing parameters and results, downloading outputs, and deleting an analysis. For custom analysis function uploads, stay tuned for the next video.
Upload Custom Analysis Functions#
View Available Analysis Functions:
Go to the Analysis tab to see all analysis functions.
Three types of functions are available:
Device Analysis for devices under test
Aggregated Die Analysis for dies
Aggregated Wafer Analysis for wafers
For each function, you can view the ID, version, and code.
Download Analysis Function:
To download a specific version, click the Download button.
When triggering an analysis, the latest version is used by default.
Upload a Custom Analysis Function:
Click New Function to upload custom code.
All code is validated to ensure safety before execution.
Modify Existing Code:
Download the code (e.g., for the device power envelope analysis).
Update the code or adjust default parameters as needed.
After modifications, select the updated file, specify the device data type, and upload it.
Test and Validate:
To apply the function to specific data, locate the Target Model Primary Key of the measurement.
Test the function with various input parameters to preview the output.
Once validated, the function is added to the database and ready for use.
Trigger and Review Analysis:
Trigger the analysis on uploaded data and review the results.
In this video, we covered uploading analysis functions, downloading and modifying code, validating custom functions, and triggering analysis on data. For performing these actions directly in Python, stay tuned for the next video.
Upload Data and Analysis Functions Using the Python API#
Step 1: Set Up Jupyter Notebook#
Open the Jupyter Notebook Interface in the Do-Lab tab.
Run each cell to generate the layout and device table by pressing Shift + Enter. This process creates both the GDS file and device table CSV file.
Step 2: Generate Measurement Data#
In the next notebook, generate the measurement data required for upload.
Run all cells from the Run tab to create JSON files for each die on the wafer.
The generated files are saved in folders by wafer ID, with subfolders for each die containing JSON data for each spiral cutback length.
Step 3: Upload Data to the Platform#
In the third notebook, open a new tab to monitor the creation of a new project.
Upload the Design Manifest (device details) and Wafer Definition (wafer map).
Upload measurement data:
Use multiple threads to speed up the process (e.g., 10 threads for faster upload).
Monitor the progress bar for upload status, which depends on file size and thread count.
Step 4: Define and Upload Analysis Functions#
Types of Analysis Functions#
Device Analysis for devices under test
Die Analysis for die-level aggregation
Wafer Analysis for wafer-level aggregation
Device Analysis#
Upload the device analysis function and retrieve the primary key for a single device.
Set sample size (e.g., n=10 for averaging, or n=2 for smaller windows) and validate.
Use
validate_and_uploadto add the function to the platform.
Die Analysis#
Trigger a die analysis for 0.3, 0.5, and 0.8 micrometer waveguides.
Set the target model to die, validate, and upload.
Run 63 die analyses (21 dies x 3 waveguide widths) sequentially and view the results, including dB/cm loss based on cutback length.
Wafer Analysis#
Aggregate results by uploading the wafer analysis function.
Set spec limits to flag outliers (e.g., 3.13 dB/cm for 0.3 micrometer waveguides).
Validate and upload with these spec limits:
3.13 dB/cm for 0.3 micrometer,
2.31 dB/cm for 0.5 micrometer,
1.09 dB/cm for 0.8 micrometer.
Review yield results for each waveguide width and die, with an overview available in the web interface.
In this video, we demonstrated the use of the Python API for data and function uploads, ideal for automating processes on a lab computer or server. The next video will cover data downloads and advanced queries using Python or SQL.
Download Data Using the Python API#
Step 1: Access the Do-Lab Tab#
Open the Do-Lab tab to begin downloading data from the “Spiral” project set up in the previous video.
Step 2: Import the Do-Data Module#
Import the required data module as TD.
Execute each code cell by pressing Shift + Enter.
Understanding the Database Structure#
Projects: Every chip belongs to a project.
Cells: Entities within a project.
Devices: Each cell instantiation is a device with X, Y coordinates.
Device_data: Stores testing or simulation data.
Analysis: Device data, dies, or wafers can be analyzed with specific functions.
Data relationships are maintained by linking wafers to projects and dies to wafers.
Step 3: Querying Data with GetDataByQuery#
Use the GetDataByQuery function to filter data with specific clauses.
For example, filter by project ID (e.g., “Spirals”) to retrieve relevant data.
Filter data by project ID and device ID to obtain device-specific data.
This data can be stored in a Pandas DataFrame with columns like wavelength and output power for easy analysis and plotting.
Additional Metadata:
View metadata such as device ID, die origin (x, y coordinates), wafer ID, set ID, and parent cell.
Building a Flat Table for Analysis Tools#
Combine device data into a single DataFrame to create a large flat table.
This table is suitable for exporting to analysis tools like JMP or Excel for further insights.
Performing Advanced Queries#
Use the API’s attribute filter for advanced data retrieval.
Filter by specific cell attributes (e.g., settings embedded in GDS or uploaded via CSV).
Example: Query devices with a 0.3-micron width for a specific project and die, yielding six devices with this width.
Refining Queries with OR Conditions:
Use OR conditions to filter data from specific dies or other attributes like length or width.
Combining Conditional and Attribute Clauses#
Combine conditional and attribute clauses to create complex queries—all without writing any SQL.
In this video, we demonstrated downloading data using Python, retrieving attributes, performing advanced filtering, and building tables for analysis in tools like JMP or Excel.