Characterize your silicon based on the expectations from the foundry model. Data from embedded monitors such as ring oscillators can be used to identify potential bottleneck cells or class of cells (e.g., specific Vt, drive strength, cell type, etc.) that are causing low margin and timing fails. 

Understand the manufacturing stability of advanced process nodes and validate how accurately the pre-silicon models can predict post-silicon parametric results. The calibration improves consistency between the behavior predicted by the design models and the actual performance of the hardware.

Gain insights into in-silicon health, visibility, and insight at each stage of the semiconductor lifecycle. Sensors and monitors include ring oscillators and path margin monitors that enable gathering in-ramp silicon results, comparing and correlating these results to design targets, and producing insights that lead to data driven actions for design, fabrication, production test, and system level test.

Improve the correlation between a predicted Vmin that comes from a model versus the measured Vmin. Silicon.da analytics solution enables the device to operate at lower power while still meeting the required performance by setting the device specific Vmin during testing on the test floor at the value of the predicted model.

Detect early silicon issues, perform remote in-system debug and diagnostic, and monitor degradation over time. SLM monitors and in-system scan test transform silicon health data into actionable insights, helping reduce failure rates across the system lifecycle and improving uptime, system longevity, and overall reliability.