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Automated assembly of high-density carbon fiber electrode arrays for single unit electrophysiological recordings.

Tianshu DongLei ChenParas R PatelJulianna M RichieCynthia A ChestekAlbert J Shih
Published in: Journal of neural engineering (2023)
Objective. Carbon fiber (CF) is good for chronic neural recording due to the small diameter (7 µ m), high Young's modulus, and low electrical resistance, but most high-density carbon fiber (HDCF) arrays are manually assembled with labor-intensive procedures and limited by the accuracy and repeatability of the operator handling. A machine to automate the assembly is desired. Approach. The HDCF array assembly machine contains: (1) a roller-based CF extruder, (2) a motion system with three linear and one rotary stages, (3) an imaging system with two digital microscope cameras, and (4) a laser cutter. The roller-based extruder automatically feeds single CF as raw material. The motion system aligns the CF with the array backend then places it. The imaging system observes the relative position between the CF and the backend. The laser cutter cuts off the CF. Two image processing algorithms are implemented to align the CF with the support shanks and circuit connection pads. Main results. The machine was capable of precisely handling 6.8 μ m carbon fiber electrodes (CFEs). Each electrode was placed into a 12 μ m wide trenches in a silicon support shank. Two HDCF arrays with 16 CFEs populated on 3 mm shanks (with 80 μ m pitch) were fully assembled. Impedance measurements were found to be in good agreement with manual assembled arrays. One HDCF array was implanted in the motor cortex in an anesthetized rat and was able to detect single unit activity. Significance. This machine can eliminate the manual labor-intensive handling, alignment and placement of single CF during assembly, providing a proof-of-concepts towards fully automated HDCF array assembly and batch production.
Keyphrases
  • high density
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  • high resolution
  • machine learning
  • high throughput
  • high speed
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  • neural network