Efficient, automated escape routing for high-density pad grids: deepest-exit-first algorithm for layer and wire count...

Area of Science:

• Neuroscience
• Electrical Engineering
• Materials Science

Background:

• The demand for higher channel count neural interfaces is limited by connector size and fabrication constraints.
• Thin-film substrates offer potential but face challenges in routing dense electrode grids.
• Current methods create a trade-off between channel count, device size, and flexibility.

Purpose of the Study:

• To develop an efficient algorithm for routing dense pad grids in neural interfaces.
• To overcome fabrication limitations and enable higher channel counts in implantable devices.
• To address the challenge of mismatched pad density and metal feature size capabilities.

Main Methods:

• Proposed a novel algorithm for efficient routing of dense pad grids, even in worst-case scenarios.
• Demonstrated the algorithm's application on a 1024-channel electrode connected to a wireless recording integrated circuit.
• Compared the algorithm's performance against standard routing methods.

Main Results:

• The algorithm can route the theoretical maximum number of traces for large grids.
• Achieved improved efficiency in routable traces, number of layers, and footprint area compared to standard methods.
• Successfully applied to a 1024-channel neural interface design.

Conclusions:

• The developed algorithm efficiently routes dense grids, crucial for high-channel-count neural interfaces.
• This method shows promise for future very large channel count device designs.
• Automates the design process, accelerating iteration for neural interface development.