The real bottleneck isn’t the layout; it’s decoding those half-hidden specs stuffed into a PDF.
Every electronics engineer and PCB designer knows the feeling: the design is done, the data package is zipped, and the request for quote (RFQ) is sent. And then ... you wait.
This is the quoting “black box.” A project’s momentum comes to a halt, sometimes for days, as you wait for a price. When the quote finally arrives, it might come with design for manufacturability (DfM) queries, unexpected costs or lead times that jeopardize the entire schedule.
How a longtime PCB supplier became a contract manufacturer.
More than a handful of US-based printed circuit board fabricators offer some degree of assembly in order to meet customer demand. Often, these companies are flex circuit manufacturers which add in-house SMT as a strategic advantage so they can offer a one-stop supply model.
Recently, however, a Chicago-area supplier of bare PCBs took a different approach: It acquired, of all things, a full-service EMS company.
And practical steps to exorcise the chemistry demons.
Each fall brings ghosts, goblins and, if you’re not careful, a few monsters lurking right inside your plating tanks. They won’t knock on your door or ring a bell, but they will hide in your agitation systems, anodes and cables. If ignored, they can turn a good product into scrap before you even notice.
Good engineering isn’t just about power; it’s about knowing where not to over-engineer.
As modern electronic devices combine RF, high-speed digital and power circuitry on a single PCB, the demand for tailored electrical performance continues to increase. A hybrid material stackup is often adopted to meet such mixed-signal requirements, especially in RF applications where signal integrity, controlled impedance and low dielectric loss are crucial.
Optimizing interconnects, fanouts and signal structures before schematic capture.
As printed circuit boards (PCBs) grow denser, faster and more power-constrained, designers face mounting challenges maintaining signal integrity, power efficiency and manufacturability. Traditionally, most optimization occurs after schematic capture – during placement and routing – when it’s often too late to remove structural inefficiencies.
How ANOVA helps ensure process data lead to accurate, defensible decisions.
The two-sample t-test determines if two population means are equal. Typical applications involve testing whether a new process or treatment outperforms a current one. But what if we have three or more means we want to test? The t-test is inappropriate for this analysis.
For example, a young engineer tests the mean brightener concentration in their four acid copper pulse plating tanks (A, B, C, D). There are six pairwise comparisons: AB, AC, AD, BC, BD, CD. Using the t-test, if the probability of correctly accepting the null hypothesis for each test is 1 – α = 0.95, then the probability of correctly accepting the null hypothesis for all six tests is (0.95)6 = 0.74, or 74%. In other words, 1 – 0.74 = 26% chance of committing a Type I error. Recall that a Type I error occurs when we reject a true null hypothesis (no statistical difference) and claim that there is a statistical difference. The multiple comparisons cause a significant increase in Type I errors. The appropriate procedure for testing the equality of several means is the analysis of variance.1
