Device Modeling MQA

About PathWave Model QA (MQA)

PathWave Model QA (MQA) is a collection of comprehensive SPICE model validation procedures, interfaces, and utilities that provide the ability to thoroughly check SPICE model quality and automate QA and reporting procedures for both silicon and III-V technologies. 

Given today's nanoscale process technologies and increasing model complexities, validating SPICE models is a significant challenge and can be very time-consuming. However, SPICE modeling engineers and model users still want the models to be thoroughly checked and the model characteristics to be easily obtained. MQA satisfies this critical industry requirement by rigorously checking the model quality, plotting model characteristics, and customizing the output targets with comprehensive checking rules while employing easy-to-use interfaces and utilities.

Key Features

• Integrate a comprehensive set of rules to ensure SPICE model quality while overlaying measurements and simulation results

• Support for different commercial simulators to ensure the industry-standard simulation result.

• Rules and checking functions are flexible and fully customizable

• Customize user-defined plots and tables with Python script

• Measurement QA, model comparison, corner model QA, and Monte Carlo analysis

• Powerful plotting functions and utilities

• The open interface enables flexible support of models, simulators, and checking routines

• Complete parallelism support on different levels: simulator level, rule level, and project level

• Automatic QA report generation and customization

• Native support of MDM and MEA data formats

• Equipped with internal SPICE engine for fast simulation

• Easy project template management allows to re-use the configurations and build a custom model qualification process

MQA Specifications

MQA is a unique software product developed to solve the following problems:

• SPICE model validation is becoming increasingly important and significantly more difficult. 

-       As the channel scales down, second-order physical effects make device modeling more complex.

-       Macro(subckt) models and binning models are used extensively.

-       Validating these models is much trickier than global models.

-       A natural consequence of the foundry business requires a better way of communicating between modeling engineers and designers. Designers often need to check whether the models satisfy their requirements for some specific circuit design needs.

-       What appears to be a good model for certain applications can be a terrible one for others.

• Model validation involves much more than just overlaying the measurement results to the simulation results of the model.

-       The measurement is limited to the number of physical devices in the test structure and the resolution of instruments.

-       Model validation should include the following checks:

- Accuracy of the model (compare with measurement).

- Completeness of the model (have all the major physical effects important to the design been modeled?).

- Mathematical robustness of the model (no kink in first and second derivative).

- The capability of the model to predict physical trends (very important in design optimization).

- Model simulation results using benchmark circuits.

• Model validation should be automatic and customizable.

-       The quality can only be guaranteed after fixed QA procedures are in place.

-       Manually validating a model is nearly impossible, considering a large number of checks for different device sizes, temperatures, and bias conditions.

-       Model reporting is often

-       time-consuming and should be expedited.

-       Model QA routines often change with model modifications; a customizable QA platform is needed.

-       QA tools should help users debug model issues and point out potential problems.