Tile types:

• Blue metal: “wires”
• White void: no material present
• Orange unknown: could not decide if metal or void

Left click: metal operation

• Void ⇒ metal
• Unknown ⇒ metal
• Metal ⇒ unknown

Right click: void operation

• Unknown ⇒ void
• Metal ⇒ void
• Void ⇒ unknown

Shift click: do above operation on all adjacent tiles (self inclusive) of the same tile type

Enter/return: submit

Escape: submit but flag for administrative review. Use when CV failed beyond reasonable human correction (ex: grid detected very poorly)

M: convert all unknowns to blue metal

W: convert all unknowns to white void

An integrated circuit is composed of metal connecting various areas together to form nets. We want to recover netlists from various chips but so far all efforts have revolved around manually drawing polygons from microscope images. A few people have tried to do some computer vision (CV) approaches but ultimately fixing the errors takes longer than drawing the original polygons.

Can we do better? Sort of…we can cheat (“cheating is a valid engineering strategy”). We cheat by using a confocal microscope (or other high contrast imager) on a chip laid out in a grid. This simplifies the problem dramatically:

• The system is now regenerative: errors can be corrected
• Grid ⇒ simple algorithms determine if metal or not in that square

However, this computer vision process is still far from perfect and generates a lot of uncertainties.

The left area is the

Left clicking is metal oriented: it turns tiles into metal. if the tile is already metal, it turns it into unknown. Similarly, right clicking is void oriented.

Above: before and after shift right clicking to remove entire upper left unknown group

Sometimes its useful to change entire clusters at once. This can be accomplished by shift clicking.

Above: using “M” to turn all unknowns into metal

Often the entire remaining unknowns will below to one type or another. Hit “M” to change all unknowns to metal or “W” to change all unknowns to void.

Above: minor grid error

If the grid is detected incorrectly it will disrupt metal detection. In the above example, the Y grid offset is very poor (although the pitch is correct). Correct errors like above by biasing the grid to a particular direction, using the same bias for the entire image. For example, make the lower unknowns metal and the upper unknowns void. Suggest using this convention (lower ⇒ metal, upper ⇒ void) if its unclear which direction the correction should be applied.

For major errors (ex: grid detection failed) press Escape to skip this tile. It will be reviewed by an administrator.

If you see this the server has no jobs available

NOTE: the splash page can be edited with standard mouse and keyboard shortcuts. Attempting to submit discards changes

The GUI frontend will become some sort of web app so that it can be used from a standard browser without installing any software.

There's still a lot that can be done with the computer vision preprocessing. The current suite is somewhat optimized for the references images: to be seen how well it will apply to other data sets (ex: SEM images).

• Source code