Granted Patent
B2
US 12,422,399 · App. 18/662,578 · Granted Sep 23, 2025
Systems, devices, and methods for gas sensing
Assignee:
Massachusetts Institute of Technology
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Abstract
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.
Claims (30)
1. A method of diagnosing ventilator-associated pneumonia (VAP) with a device 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:
culturing bacteria from a patient susceptible to VAP;
detecting at least one volatile organic compound (VOC) in emissions of the bacteria with the CNFETs in the sensing layer;
writing data generated by the CNFETs in parallel to memory elements in the memory layer;
transferring the data generated by the CNFETs in parallel from the memory elements to processing elements in the processing layer;
identifying, with the processing elements, the at least one VOC in the emissions of the bacteria based on the data generated by the CNFETs; and
diagnosing the patient as having VAP based on the at least one VOC in the emissions of the bacteria.
2. The method of claim 1 , wherein the device is embedded in or coupled to a culture dish used for culturing the bacteria.
3. The method of claim 1 , wherein the detecting the at least one VOC further comprises:
detecting, by the processing layer, a change in a response pattern of the CNFETs.
4. The method of claim 1 , further comprising identifying at least a first bacterial type in the bacteria from the patient by:
generating, by the processing layer, a set of images, each image of the set of images corresponding to a response pattern of a subset of the CNFETs to the emissions of the bacteria; and
classifying, using supervised learning, the set of images as the first bacterial type of a predetermined set of bacterial types.
5. The method of claim 4 , where the predetermined set of bacterial types includes one or more of: Escherichia coli, Proteus mirabilis, Moraxella catarrhalis, Serratia marcescens, Klebsiella pneumoniae, Burkholderia cepacia, Acinetobacter baumannii, Streptococcus pneumoniae, Stenotrophomonas ( Xanthomonas ) maltophilia, Aspergillus niger, Neisseria lactamica, Streptococcus pyogenes, Pseudomonas aeruginosa, Staphylococcus aureus , or Haemophilus influenzae.
6. The method of claim 4 , wherein the supervised learning includes using one or more of: a support vector machine (SVM), artificial neural network, decision tree, or random forest.
7. The method of claim 6 , wherein the supervised learning includes a SVM that has been previously trained on training response patterns associated with the CNFETs of the device.
8. The method of claim 4 , wherein the CNFETs are disposed as an array of CNFETs on the sensing layer, and wherein each image of the set of images corresponds to a different subarray of the array of CNFETs, and wherein each subarray of the array of CNFETs has a different functionalization from each other subarray of the array of CNFETs.
9. The method of claim 8 , wherein the functionalization of each CN FET of the array of CNFETs is non-specific to the at least one VOC to be detected.
10. The method of claim 4 , further comprising:
training a machine learning model to identify the at least one VOC based on the set of images.
11. The method of claim 1 , wherein the detecting the at least one VOC in the emissions of the bacteria with the CNFETs in the sensing layer comprises adjusting a bias voltage applied to at least one of the CNFETs.
12. The method of claim 1 , wherein the detecting the at least one VOC in the emissions of the bacteria with the CNFETs in the sensing layer comprises recording current-voltage characteristics of the CNFETs.
13. The method of claim 1 , wherein the writing the data in parallel from the CNFETs to the memory elements in the memory layer comprises transmitting the data via interlayer vias connecting the sensing layer to the memory layer.
14. The method of claim 1 , wherein the 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.
15. The method of claim 1 , wherein at least one of the CNFETs is functionalized with multiple functional materials.
16. The method of claim 1 , wherein the CNFETs are arranged in blocks and each of the blocks is functionalized with a different functional material.
17. The method of claim 16 , wherein the different functional materials include at least 50 different functional materials.
18. The method of claim 1 , wherein the device includes at least 1,000,000 CNFETs.
19. The method of claim 1 , wherein the memory elements comprise resistive random access memory (RRAM) cells.
20. The method of claim 1 , wherein the sensing layer, memory layer, and processing layer form a monolithic, three-dimensional integrated circuit.
Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST
Recorded May 21, 2024
From: SHULAKER, MAX; BISHOP, MINDY DEANNA
To: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Reel/Frame 067480/0938 →
Continuity (4)
Division
16973249
Provisional Application
62682497
· Jun 8, 2018
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