When you consider the technical issues of making semiconductors, it seems impossibly difficult. Semiconductor fabrication requires lithographic processes to create features that are measured fractions of an Angstrom, the unit of measure of wavelengths of light. Pretty small. The least contamination or vibration that isn’t supposed to be there can ruin parts.
Wafer polishing machines must polish the slices of silicon to a flatness and perfection that can’t be measured by conventional means. Multi-axis robots handle silicon wafers in vacuum chambers without putting the tiniest scratch on the surface. Wafer cassettes with $250 to $500K worth of uncut chips have to be shuttled from process machine to process machine inspected and tested for defects.
The materials are among the most exotic in the world; gold interconnects, high purity copper, pure silicon crystals that are grown in furnaces at temperatures in excess of 2600 degrees Fahrenheit. Processes that require chemicals with extraordinary purity, some of which are the most corrosive acids on the planet.
The shiny little sliver of crystalline silicon can embed the intelligence of man-years of programming, sense the position of an actuator to millionths of an inch, measure current in a conductor or regulate lethal amounts of voltage or current in power semiconductor applications. Pretty amazing stuff.
And when it comes to controlling motion, many solutions are available. Digital Signal Processors have been one of the key technologies for controlling motion because of their ability to process mathematical models of analog events. Field Programmable Gate Arrays that host huge arrays of logic gates can read encoder feedback and execute control tasks at incredible speed. Microprocessor based motor controls and servo systems have been around for a decade or more. And the latest generation of microcontrollers offers to integrate the power of the DSP with the flexibility of a microprocessor and multitasking needed to support network communications to other devices.
What’s a bit strange is that we keep solving the same problems over and over using different platforms. Why haven’t we found the ideal solution? At a certain point, its the same mathematical model of a real world phenomenon that we are trying to run. Isn’t it? Or are we solving different problems and needing to find better hardware solutions? Or are the tools evolving to make treatment of the complexities easier?
A little bit of all of these. And maybe that’s what makes it so interesting.
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