MEDICAL APPLICATIONS

Implantable magnetic resonance sensor

Implantable MR Sensor

NMR-Needle: co-integration of a miniaturized Magnetic Resonance (MR) detection coil with the complete MR transceiver on a single implantable sensor

Technology: ST 130nm BiCMOS9MW

Frederik Dreyer, Daniel Krüger, Jens Anders

Increasing sensitivity and signal robustness

 

The presented chip was designed to conduct in-vivo magnetic resonance (MR) experiments inside rat brains and to offer superior sensitivity and signal robustness compared to extracorporeal detection coils and passive implantable MR coils.

Designing the NMR-Needle

 

The so-called NMR-on-a-chip approach [1] suggests co-integrating a miniaturized MR detection coil with the complete MR transceiver on a single implantable sensor.

 

The on-chip MR coil is implemented in the two thick top metal layers provided by ST’s BiCMOS9MW technology. By connecting multi-turn coils in the two layers in series, the induced MR signal can be maximized to alleviate the burden on the receiver electronics.

 

To allow for implantation with minimum tissue damage, the dies are post-processed by mechanically thinning them down to 80µm and producing a tip at an optimized angle by wafer dicing.

Improving performance and application range

 

This chip is the next generation of the device presented in [2], further improving its performance and application range in the target MR application as well as minimizing tissue damage during the implantation surgery by using an optimized chip geometry. Applications of the device include, for instance, the detection of changes in blood flow and oxygenation as well as in-vivo MR imaging and heteronuclear MR spectroscopy.

Micrograph of the post-processed die.

Block diagram of the ASIC.

References

 

[1] J. Anders, F. Dreyer and D. Krüger, “On-Chip Nuclear Magnetic Resonance,” in Handbook of Biochips: Integrated Circuits amd Systems for Biology and Medicine, Springer New York, 2020, pp. 1-32

 

[2] J. Handwerker et al. “A CMOS NMR needle for probing brain physiology with high spatial and temporal resolution” Nature Methods 17.1 (2020), pp. 64-67