I found this patent for TDK involving NN however I don’t have the expertise to interrogate it. I’ll leave that to DIO.
appft.uspto.gov
United States Patent Application | 20200371076 |
Kind Code | A1 |
Koenig; Matthias | November 26, 2020 |
Method and Apparatus for Operating a Multi-Gas Sensor
Abstract
A method and apparatus for operating a multi-gas sensor are disclosed. In an embodiment, a method includes providing at least one calibration input comprising sensor design data of the multi-gas sensor, which varies dependent on production process parameters, and/or sensor production process parameter data of the multi-gas sensor, and/or measurement results of the multi-gas sensor captured when the multi-gas sensor is exposed to one of the gases or a gas mixture to be detected and/or sensed by the multi-gas sensor; providing a trained neural network including an input layer with K input nodes, an output layer with L output nodes and at least one hidden layer; storing each calibration input as a fixed input to a corresponding input node of the trained neural network; and providing a multi-gas sensor output for at least a part of the gases to be detected and/or sensed by the multi-gas sensor dependent on the trained neural network and actual measured sensor values from the sensor elements.
Inventors: | Koenig; Matthias; (Muenchen, DE) |
Applicant: |
Name | City | State | Country | Type |
---|
TDK Electronics AG |
Munich | |
DE
| |
|
---|
Family ID: | 73052967 |
Appl. No.: | 16/878167 |
Filed: | May 19, 2020 |
The input signals are the activations, the Calibration inputs are the weights.
The trick of this invention is to enable gas sensors to be calibrated from sampling only a few of the target gasses using a NN.
There is no description of an NPU, so the NN is one someone baked earlier.
US2020371076A1 Method and Apparatus for Operating a Multi-Gas Sensor
1 . A method for operating a multi-gas sensor comprising multiple sensor elements, wherein the multi-gas sensor is configured to detect and/or sense a predefined number M of different gases, the method comprising:
providing at least one calibration input comprising:
sensor design data of the multi-gas sensor, which varies dependent on production process parameters; and/or
sensor production process parameter data of the multi-gas sensor; and/or
measurement results of the multi-gas sensor captured when the multi-gas sensor is exposed to one of the gases or a gas mixture to be detected and/or sensed by the multi-gas sensor;
providing
a trained neural network comprising an input layer with K input nodes, an output layer with L output nodes and
at least one hidden layer, wherein L, M and K are natural numbers;
storing each calibration input as a fixed input to a corresponding input node of the trained neural network; and
providing a multi-gas sensor output for at least a part of the gases to be detected and/or sensed by the multi-gas sensor dependent on the trained neural network and actual measured sensor values from the sensor elements, which are provided to corresponding input nodes of the trained neural network.
[0030] The respective gas sensor element
10 shown in FIG. 1 comprises for example
a sensing layer 11 of metal oxide. The gas sensor elements
10 are, for instance, integrated with a CMOS circuitry (not shown) on a single chip. A stack of layers
13 is arranged on a semiconductor substrate
14 required for the CMOS circuitry. The respective gas sensor element
10 comprises a membrane. A portion of the semiconductor substrate
14 is, for instance,
etched away to form a cavity 12 at the location of the sensing layer 11 . Remaining layers
13 and possibly a remaining portion of the substrate
14 form a thin membrane to support the layer
11 .
[0031] The respective sensor element
10 comprises a
heating element 15 . The heating element
15 is embedded within the layer
13 and comprises conducting elements. The heating element
15 is configured to provide a local source of heat to
heat the metal oxide layer 11 e.g., during operation of the gas sensor element
10 . The temperature can rise rapidly around the metal oxide layer
11 on the membrane, while a thicker part of the gas sensor chip, i.e. the portion where the substrate
14 is not removed, reacts with a slower rise of temperature due to its thermal inertia. By controlling the heating element
15 accordingly, the metal oxide layer
11 can be activated for a measurement and be regenerated afterwards.
[0032]
Each of the metal oxide layers 11 is contacted by two conductive electrodes and hence acts as a resistor.
In the presence of a compound its resistance changes, thereby providing a measure of a concentration of the compound in the immediate vicinity of the metal oxide sensing layer
11 . The change of the resistance and/or impedance can be measured by a voltage measurement.
[0035] Gas sensors have to be calibrated. The output signals of the gas sensor elements
10 are generally in the form of a voltage value.
Calibration is needed to implement a relation between the gas sensor element 10 signal and the concentration level of the corresponding gas.
[0038]
Because of manufacturing tolerances it is not possible to produce exact copies of a gas sensor in a production process. There are always small fluctuations in the provided output signals. This is the reason why
nearly all gas sensor products need to be calibrated after assembly. This means that calibration data is determined and used during operation of the gas sensor to adjust the sensor signals of the sensor elements
10 to provide accurate measurement output signals.
[0046] Embodiments provide relevant or most relevant calibration information and perform
some gas exposure tests, i.e. not for all gases but only for some, and train the neural network N such that the neural network N can be used to provide the calibration also for other untested gases.