The human eye soon tires of this and dead pixels are then missed.
Obviously, an automated system is the solution but we could not find an off the shelf solution, so we had to build a Machine Vision-based one using a camera and Vision Option software from National Instruments to detect dead pixels.
The first question was whether or not to test the incoming displays from the supplier or to test them once assembled into products or at both stages. Doing both would have doubled the costs as two sets of cameras and optics would have been required along with two software licenses.
We therefore decided that the most cost effective solution was to test the assembled product at the functional test stage, as this would highlight both any faults in the display's pixels and any faults that had occurred in the assembly of the control electronics.
This would result in a simple pass or fail for each unit. A fail could be broken into two types. Random pixel failures meant that the display had dead pixels and would be sent back to the supplier. Groups of pixel failures in certain patterns meant that the control electronics had a fault for us to fix.
However, when we set this all up, the camera showed strange coloured patterns, which meant that it was impossible to work out if any pixels were malfunctioning. These are Moiré patterns caused by the lines of sensors in the camera’s CCD optically interacting with the lines of pixels in the display.
Even the slightest difference between the line-up of the two sets of parallel lines resulted in Moiré patterns. A common example of the Moiré effect is a bridge over a motorway where the vertical railings on the near and far sides create a changing pattern as you drive towards the bridge.
Fig. 1: Moiré patterns.