Monolithic 3D integrated circuit for gas sensing and method of making and system using
A monolithic, three-dimensional (3D) integrated circuit (IC) device includes a sensing layer, a memory layer, and a processing layer. The sensing layer includes a plurality of carbon nanotube field-effect transistors (CNFETs) that are functionalized with at least 50 functional materials to generate data in response to exposure to a gas. The memory layer stores the data generated by the plurality of CNFETs, and the processing layer identifies one or more components of the gas based on the data generated by the plurality of CNFETs.
1. A method of diagnosing ventilator-associated pneumonia (VAP) with a monolithic, three-dimensional (3D) integrated circuit (IC) comprising carbon nanotube field-effect transistors (CNFETs) arranged in a sensing layer, a memory layer operably coupled to the sensing layer, and a processing layer operably coupled to the memory layer, the method comprising:
sensing an exhalation of a patient with the CNFETs;
writing data in parallel from the CNFETs to memory elements in the memory layer;
transferring the data in parallel from the memory elements to processing elements in the processing layer;
detecting, with the processing elements, at least one volatile organic compound (VOC) representative of VAP in the exhalation of the patient based on the data; and
diagnosing the patient as having VAP based on the at least one VOC detected in the exhalation of the patient.
2. The method of claim 1 , wherein writing the data in parallel from the CNFETs to memory elements in the memory layer comprises transmitting the data via interlayer vias connecting the sensing layer to the memory layer.
3. The method of claim 1 , wherein transferring the data in parallel from the memory elements to the processing elements comprises transmitting the data via interlayer vias connecting the memory layer to the processing layer.
4. The method of claim 1 , wherein detecting the at least one VOC further comprises:
detecting, by the processing layer, a change in a response pattern of the CNFETs.
5. The method of claim 1 , further comprising:
determining a change in the at least one VOC with respect to a baseline exhalation of the patient; and
determining that the patient has VAP based on the change in the at least one VOC.
6. The method of claim 5 , wherein the at least one VOC includes a plurality of VOCs, and wherein determining the change in the exhalation includes determining a change in composition of the plurality of VOCs in the exhalation.
7. The method of claim 5 , wherein the at least one VOC includes a plurality of VOCs, and wherein determining the change in the exhalation includes determining a change in concentration of the at least one VOC of the plurality of VOCs in the exhalation.
8. The method of claim 1 , wherein the monolithic, 3D IC is positioned within a ventilator coupled to the patient.
9. The method of claim 8 , wherein the monolithic, 3D IC is positioned within an expiratory circuit of the ventilator.
10. The method of claim 1 , wherein the monolithic, 3D IC is positioned within an endotracheal tube coupled to the patient.
11. The method of claim 1 , wherein the monolithic, 3D IC is coupled to a bronchoscope inserted into a lung of the patient.
12. The method of claim 1 , wherein at least one of the CNFETs is functionalized with multiple functional materials.
13. The method of claim 1 , wherein the CNFETs are arranged in blocks and each of the blocks is functionalized with a different functional material.
14. The method of claim 1 , wherein sensing the exhalation of the patient with the CNFETs comprises generating measurements of the exhalation with the CNFETs at each of a plurality of biasing conditions.
15. The method of claim 1 , wherein sensing the exhalation of the patient with the CNFETs comprises forming an image representing responses of the CNFETs to components of the exhalation.
16. The method of claim 15 , wherein detecting the at least one VOC comprises classifying the components of the exhalation with a machine learning classifier implemented by the processing layer.