Semiconductor process engineers would love to develop successful process recipes without the guesswork of repeated wafer testing. Unfortunately, developing a successful process can’t be done without ...

Accurate and predictive process modeling, in combination with virtual metrology enables the characterization of any feature on any given structure, is becoming a key requirement in advanced technology ...

Planning to move your organization into a more digital environment can often be complex, but it’s happening whether you like it or not. Business process modeling can help you ...

PSE’s gPROMS is a unified equation-oriented process modeling environment for applications across the plant, from complex catalytic reaction and separation to wastewater treatment and utilities.

In biopharmaceutical manufacturing the interactions between cells, nutrients, and reagents in culture determine product quality. The big challenge for process developers is modeling these complex ...

Gaussian process (GP) models are widely used to approximate time consuming deterministic computer codes, which are often models of physical systems based on partial differential equations (PDEs).

For process improvement practitioners, Monte Carlo simulation is an important tool and method to reduce risk and facilitate those decisions grounded on data and evaluation. Mark Sidote is a Principal ...

Liping Liu, Lining Jiang, Ding Zhang, An integrated model of statistical process control and condition-based maintenance for deteriorating systems, The Journal of the Operational Research Society, Vol ...