Questions we get asked before a sample ships.

How can we figure out where metal contamination is coming from in our production line?

Metal contamination can sneak in from multiple places at once — often hiding in your raw manufacturing consumables or the process water you are using. We use ICP-MS to help clients pinpoint these issues down to the parts-per-billion (ppb) level. By testing both your incoming water sources and individual supply materials, we isolate the exact root cause so you can fix the contamination at the source.

We are having trouble identifying microscopic surface contaminants. How does TOF-SIMS help?

TOF-SIMS is one of the most powerful tools available for deep surface analytics. When you are dealing with unknown residues, surface defects, or chemical changes in the outermost atomic layers of your material, TOF-SIMS maps out the exact chemical composition. It tells you not just that a contaminant is there, but precisely what it is and how deep it penetrates.

How can we inspect nanoscale structures and get a breakdown of their elemental composition at the same time?

That is the exact strength of SEM-EDX. Scanning Electron Microscopy gives you ultra-high-resolution imaging to view tiny nanostructures and particle sizes, while EDX maps out the elemental breakdown. Together, they let you zoom in on a microscopic defect and instantly tell you what chemical elements it is made of.

How do we identify unknown surface defects and figure out their source using molecular analysis?

FTIR acts as a chemical “molecular fingerprint.” When an unexpected residue or defect appears on your product, FTIR scans it and matches the light absorption pattern against known chemical databases. This helps you compare defective areas against clean surfaces to uncover whether the issue came from an adhesive breakdown, a cleaning residue, or environmental exposure.

Why do we need both emission and excitation spectra in fluorescence spectroscopy? Aren’t they measuring the same thing?

Not quite — they look at two sides of the same question. The excitation spectrum tells you what wavelengths of light are required to get your material to glow or react. The emission spectrum shows you what light wavelengths your material actually gives off in return. Looking at both gives you a complete fingerprint of the sample’s optical and electronic properties.

Why is viscosity testing so critical for formulations like sauces, paints, and polymers?

Viscosity isn't just about how “thick” something is; it dictates how your product behaves under real-world stress — whether it's pouring a food sauce smoothly, brushing paint evenly without dripping, or molding a complex polymer. We test how formulations react to changing shear rates and temperatures so you can guarantee batch-to-batch consistency.

We are struggling with poor edge quality and too much burr and roughness during laser dicing. How do we fix this?

Edge quality issues usually stem from a mismatch in process parameters. Solving a dicing problem isn't just about turning the laser on — it requires balancing cutting speed, laser power, and thermal management. If heat isn't dissipated properly, you get thermal damage and rough edges. We help optimize these variables to give you clean, precise cuts with minimal heat-affected zones.

Can you help us redesign a current PCB layout to resolve performance or component bottlenecks?

Yes, absolutely. We regularly help clients redesign PCBs from scratch or modify existing layouts. By evaluating your component list, functional goals, and the complexity of your layer stack-up, we provide actionable recommendations to improve signal integrity, resolve routing constraints, and streamline manufacturability.

We are running into a delamination issue with our optical filters. How do we ensure stack quality for DBR mirrors?

Delamination in Distributed Bragg Reflector (DBR) mirrors usually comes down to structural stress within the layer stack or an incompatible growth process. We provide direct consultancy on custom optical filter designs and guide you through choosing the right growth technique. Getting the stack architecture and interface adhesion right is essential to preventing peeling and ensuring long-term optical durability.

How can machine learning predict our manufacturing outcomes, and which parameters actually matter?

Machine learning can drastically reduce trial-and-error by predicting process outcomes, but its success relies entirely on feeding it the right process parameters. We help you identify and isolate the variables that truly drive performance — such as temperature curves, gas flow rates, material batch variations, and power settings. Once those core parameters are defined, predictive models can forecast defects or optimize yields before you run a full production batch.