Abstract
Abstract
We report on a novel technology for microfabricating 3D origami-styled micro
electro-mechanical systems (MEMS) structures with glassy carbon (GC) features
and a supporting polymer substrate. GC MEMS devices that open to form 3D
microstructures are microfabricated from GC patterns that are made through
pyrolysis of polymer precursors on high-temperature resisting substrates like
silicon or quartz and then transferring the patterned devices to a flexible
substrate like polyimide followed by deposition of an insulation layer. The
devices on flexible substrate are then folded into 3D form in an
origami-fashion.
These 3D MEMS devices have tunable mechanical properties that are achieved by
selectively varying the thickness of the polymeric substrate and insulation
layers at any desired location. This technology opens new possibilities by
enabling microfabrication of a variety of 3D GC MEMS structures suited to
applications ranging from biochemical sensing to implantable microelectrode
arrays. As a demonstration of the technology, a neural signal recording
microelectrode array platform that integrates both surface (cortical) and depth
(intracortical) GC microelectrodes onto a single flexible thin-film device is
introduced.
When the device is unfurled, a pre-shaped shank of polyimide automatically comes
off the substrate and forms the penetrating part of the device in a 3D fashion.
With the advantage of being highly reproducible and batch-fabricated, the device
introduced here allows for simultaneous recording of electrophysiological
signals from both the brain surface (electrocorticography—ECoG) and depth
(single neuron). Our device, therefore, has the potential to elucidate the roles
of underlying neurons on the different components of
µECoG signals. For in vivo validation of
the design capabilities, the recording sites are coated with a
poly(3,4-ethylenedioxythiophene)—polystyrene sulfonate—carbon
nanotube composite, to improve the electrical conductivity of the electrodes and
consequently the quality of the recorded signals. Results show that both
µECoG and intracortical arrays were able to acquire
neural signals with high-sensitivity that increased with depth, thereby
verifying the device functionality.
Subject
Electrical and Electronic Engineering,Mechanical Engineering,Mechanics of Materials,Electronic, Optical and Magnetic Materials
Cited by
28 articles.
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