Signal Processing Software

Sensors and signals play a key role in many of our customer’s applications. More often than not, raw sensor signals exhibit one or more problems that can seriously impact the achievable performance of a system. Examples are (random) noise, drift, outliers, and sensor drop outs. SC Solutions has developed a wide variety of Signal Processing software algorithms that address these and other issues.

Noise Filtering and Outlier Detection

Many sensors require filtering to clean up the signal. SC Solutions has a large number of filters to choose from that provide just enough filtering to eliminate the noise, without adding too much phase delay. SC also has a proprietary algorithm to detect outliers and sensor drop outs and replace these with meaningful values based on system knowledge, so your process can continue to perform without being interrupted or having to tune down controllers.

Kalman Filtering

A Kalman filter is a specific noise filter that is very efficient in recursively estimating the state of a dynamic system from a series of noisy measurements. A Kalman filter can be used in conjunction with a linear-quadratic regulator (LQR) to solve the so-called linear-quadratic-Gaussian (LQG) control problem, which can be effectively used to design controllers for systems with multiple actuators and sensors that have a strong coupling between them. SC Solutions has considerable experience in both design and efficient real-time implementation of Kalman filters.

Fault Detection, Classification and Accommodation (FDCA)

Many modern manufacturing systems require increasing levels of reliability. The reliability may be provided as hardware redundancy or analytical redundancy (through model-based estimation) with the latter having obvious cost advantages. There are three functions that must be performed in any analytical redundancy scheme. These are fault detection (FD), fault classification (FC), and fault accommodation (FA). The combination of these three schemes, also abbreviated as FDCA, will lead to “predictive maintenance.” Effective FDCA applications can help detect and predict excursions of system variables and thus reduce wafer scrap, and will be an important component of the fabs-of-the-future. SC has long-standing experience with all aspects of FDCA and can help you tune or optimize your existing FDCA tools, or develop custom FDCA tools tailored to your specific needs.

Virtual Sensing

Often, systems are limited in the number and/or placement of real sensors for tracking system performance. It is then advantageous to use a simulation model of the system to define virtual sensors, i.e. use the model to reconstruct unavailable signals from available signals. Virtual sensors can be used either to supplement the real sensors for control applications, or to build FDCA analytical redundancy in data collection to minimize unscheduled system downtime.

We can deliver these algorithms as software or in hardware, as a stand-alone product, or as part of our custom control systems. Please contact us to discuss your application needs.