Plant calibration of sensors

Abstract

A method for factory calibration of sensors of a CGM system is disclosed. And the processor receives the thickness of the enzyme film after the working wire is impregnated each time according to the first parameter. The processor receives a glucose limiting film thickness after each impregnation of the working wire according to a second parameter. The processor determines a working wire diameter and includes a film thickness. The processor in communication with the CGM system automatically generates a correlation between the first parameter and the second parameter and at least one of plant sensitivity and drift characteristics. The drift characteristic predicts a sensitivity of the sensor over time. The processor associates the at least one of the plant sensitivity or the drift characteristic with the sensor. The sensor outputs a glucose reading during in vivo use based on the at least one of the plant sensitivity or the drift characteristic.

Description

[0001] Related Applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 380,392, filed on October 21, 2022, and entitled “Factory Calibration of aSensor,” which is hereby incorporated by reference in its entirety. Background Art

[0003] Medical patients often suffer from diseases or conditions that require measurement and reporting of biological conditions. For example, if a patient suffers from diabetes, it is important for the patient to have an accurate understanding of the glucose level in their blood. Traditionally, diabetics monitor their glucose levels by pricking their finger with a small lancet, forming a drop of blood, and then dipping a test strip into the blood. The test strip is positioned in a handheld monitor, which analyzes the blood and reports the measured glucose level to the patient in a visual manner. Based on this reported level, the patient makes important decisions about what food to eat or how much insulin to inject into their blood. Although it is beneficial for patients to check glucose levels multiple times throughout the day, many patients fail to adequately monitor their glucose levels due to pain and inconvenience. As a result, patients may eat improperly or inject too much or too little insulin. Either way, the patient's quality of life will be reduced, and the possibility of permanent damage to their health and body will also increase. Diabetes is a devastating disease that, if not properly controlled, can lead to poor physical conditions such as kidney failure, skin ulcers or bleeding in the eyes, and ultimately lead to blindness and pain and eventually amputation.

[0004] Regularly and accurately monitoring glucose levels is essential for diabetic patients. In order to promote this monitoring, a continuous glucose monitoring (CGM) sensor is a device that automatically measures a type of glucose by sampling a fluid in an area just under the skin multiple times a day. The CGM device generally includes an electronic device that is located therein and adheres to a small housing on the patient's skin to wear for a period of time. The small needle in the device delivers a subcutaneous sensor that is generally electrochemical. In this way, patients can install CGM on their bodies, and CGM will provide automated and accurate glucose monitoring for many days without the need for the patient or caregiver to take any action. It should be understood that, according to the needs of the patient, continuous glucose monitoring can be performed at different intervals. For example, some continuous glucose monitors can be set or programmed to obtain multiple readings per minute, and in other cases, the continuous glucose monitor can be programmed or set to obtain readings about every hour. It should be understood that the continuous glucose monitor can sense and report readings at different intervals.

[0005] Continuous glucose monitoring is a complex process, and it is known that due to several reasons, glucose levels in the blood can significantly rise / increase or rapidly drop / decrease. Therefore, a single glucose measurement only provides a snapshot of the instantaneous glucose level in the patient's body. Such a single measurement provides little information on how the patient's glucose utilization changes over time or how the patient responds to a specific dose of insulin. Even patients who adhere to a strict schedule of test strip measurements may make incorrect decisions regarding diet, exercise, and insulin injections. Of course, the situation is exacerbated when patients are less adherent to test strip measurements. To provide patients with a more comprehensive understanding of their diabetes condition and achieve better treatment outcomes, some diabetes patients are now using continuous glucose monitoring.

[0006] Electrochemical glucose sensors operate by using electrodes that typically detect the amperometric signal caused by the oxidation of an enzyme during the conversion of glucose to gluconolactone. Thus, the amperometric signal can be correlated to the glucose concentration. A two-electrode (also known as bipolar) design uses a working electrode and a reference electrode, where the reference electrode provides a reference for the working electrode relative to a bias. The reference electrode essentially completes the flow of electrons in the electrochemical circuit. A three-electrode (or tripolar) design has a working electrode, a reference electrode, and a counter electrode. The counter electrode replenishes the ion loss at the reference electrode and is part of the ion circuit.

[0007] The working wire is then associated with the reference electrode and, in some cases, with one or more counter electrodes that form the CGM sensor. In operation, the CGM sensor is coupled to and cooperates with electronics in a small housing, such as a processor, memory, radio, and power source located in the small housing. The CGM sensor typically has a disposable applicator device that uses a small insertion needle to subcutaneously deliver the CGM sensor into the patient's body. Once the CGM sensor is in place, the applicator is discarded and the electronics housing is attached to the sensor. Although the electronics housing is reusable and can be used for a long time, the CGM sensor and the applicator need to be replaced quite frequently, typically every few days.

[0008] A significant drawback of CGM sensors is known to be that they exhibit significant variability between patients and even have sensitivity variability over time for a given patient. More specifically, the sensitivity of CGM sensors to blood glucose concentration varies, and thus must be locally calibrated by each patient before use and then re-calibrated over time for a specific user. Unfortunately, the local calibration process requires the patient to prick their finger and use a standard test strip detector to obtain a blood glucose reading. Local calibration is not only inconvenient, time-consuming, error-prone, but also painful, such that patients may delay or avoid local calibration, thereby undermining any potential benefits of the CGM system.

[0009] It is known in the art that after implanting a CGM sensor, it is calibrated by the user and the sensor provides sensor data to an electronic device. The electronic device converts the sensor data such that an estimated glucose level in the blood can be continuously reported to the user. As the electronic device continues to receive sensor data, the user can recalibrate the sensor locally to account for possible changes in sensor sensitivity. Sensor sensitivity is a measure of the reaction of glucose oxidase, which produces hydrogen peroxide in the reaction and is measured directly on the working electrode surface as free electrons that generate a current. Over time, the sensitivity may change due to various factors that can affect accurate measurement, and thus it is known in the art that it is necessary to calibrate the sensor several times a day or a week. Summary of the Invention

[0010] In some embodiments, a method for factory calibration of a sensor for a continuous glucose monitoring (CGM) system is disclosed. A processor receives enzyme membrane data that includes the enzyme membrane thickness after each impregnation of a working wire in a first impregnation solution according to a first parameter for forming an enzyme membrane on the working wire. The processor receives glucose restriction membrane data that includes the glucose restriction membrane thickness after each impregnation of the working wire in a second impregnation solution according to a second parameter for forming a glucose restriction membrane on the working wire. The processor determines a working wire diameter after the working wire is formed. The working wire diameter includes the enzyme membrane thickness and the glucose restriction membrane thickness. The formed working wire includes an interference membrane, an enzyme membrane, and a glucose restriction membrane. A processor in communication with the CGM system automatically generates a correlation between the first parameter and the second parameter and at least one of a factory sensitivity and a drift characteristic of the sensor. The drift characteristic predicts the sensitivity of the sensor over time. The processor associates at least one of the factory sensitivity or the drift characteristic with the sensor. The sensor outputs a glucose reading based on at least one of the factory sensitivity or the drift characteristic during in vivo use.

[0011] In some embodiments, a method for factory calibration of a sensor for a continuous glucose monitoring (CGM) system is disclosed. A working wire is immersed in a first coating solution according to a first parameter to form an enzyme film on the working wire. Enzyme film data including the enzyme film thickness is measured. The working wire is immersed in a second coating solution according to a second parameter to form a glucose limiting film on the wire. Glucose limiting film data including the glucose limiting film thickness is measured. A processor determines the working wire diameter after the working wire is formed. The working wire diameter includes the enzyme film thickness and the glucose limiting film thickness. The formed working wire includes an interference film, an enzyme film, and a glucose limiting film. A processor in communication with the CGM system automatically generates a correlation between the first parameter and the second parameter and at least one of the factory sensitivity and the drift characteristics of the sensor. The drift characteristics predict the sensitivity of the sensor over time. The processor associates at least one of the factory sensitivity or the drift characteristics with the sensor. The sensor outputs a glucose reading based on at least one of the factory sensitivity or the drift characteristics during in vivo use. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a cross-sectional view of a working wire according to some embodiments.

[0013] Figure 2 is a process flow block diagram for manufacturing a working wire and a reference wire according to some embodiments.

[0014] Figure 3 Depicts an example of data sampling after scraping and splitting during the manufacture of a sensor wire according to some embodiments.

[0015] Figure 4 Depicts an example of data sampling after electro-polymerization during the manufacture of a sensor wire according to some embodiments.

[0016] Figure 5 is an isometric view of an immersion station with a fixture and a bucket according to some embodiments.

[0017] Figure 6 Depicts an example of data sampling after forming an enzyme film according to some embodiments.

[0018] Figures 7A to 7C Depicts an example of data sampling after forming a glucose limiting film on the first day, the second day, and the third day according to some embodiments.

[0019] Figure 7D Shows a sensitivity plot of the electrical response of a prior art CGM sensor.

[0020] Figure 8A Depicts the linearity statistics of a sensor for calibration checks according to some embodiments.

[0021] Figure 8B Depicts the sensitivity statistics of a sensor during a calibration check according to some embodiments.

[0022] Figure 8C Is a graph of the baseline of a sensor and the sensitivity of the sensor according to some embodiments.

[0023] Figure 8D Is according to some embodiments from Figure 8C A graph of the logarithm of the baseline of a sensor and the logarithm of the sensitivity of the sensor for the data in.

[0024] Figure 8E Is a graph of the in - vivo baseline and the in - vivo sensitivity according to some embodiments.

[0025] Figure 8F Is a graph of the in - vivo sensitivity drift and the in - vivo sensitivity according to some embodiments.

[0026] Figure 9A And Figure 9B Is a flowchart of the factory calibration of a sensor for a continuous glucose monitoring (CGM) system according to some embodiments.

[0027] Figure 10 Is a schematic diagram of a CGM system being used by a patient according to some embodiments.

[0028] Figures 11A to 11C Is a table detailing process improvements according to some embodiments.

[0029] Figure 12 Illustrates an automated factory software control process according to some embodiments.

[0030] Figure 13 Is a simplified schematic diagram showing an example computer system for use in a computing platform according to some embodiments. Detailed Description

[0031] This disclosure relates to performing factory calibration of a continuous glucose monitoring (CGM) system. Embodiments disclose that after calibration at the factory, no subsequent re - calibration is required. For example, once a sensor is calibrated in the factory, it does not need to be calibrated again. By eliminating the need for locally performed user calibration, there is no longer a need for frequent finger - prick methods to obtain blood glucose measurements for in - use calibration, thus allowing this method to be completely avoided.

[0032] Systems and methods for manufacturing sensor leads for continuous biosensors are described. The continuous biosensor can be, for example, a continuous glucose monitor. In some embodiments, the sensor can be a two-electrode design having a working electrode and a reference electrode, where the reference electrode provides a reference against which the working electrode is compared. The working lead includes an enzyme membrane to detect glucose levels in a patient's blood. In other embodiments, the biosensor can be a metabolic sensor for measuring other metabolic characteristics, such as ketones, lactate, or fatty acids. The sensor uses a working lead that serves as an electrode of the sensor and has a number of concentrically formed membrane layers.

[0033] In some embodiments, an automated system measures the dimensions of the working lead as the working lead undergoes an impregnation process to form membranes (also referred to as layers), and then uses these measurements to make real-time adjustments to the impregnation parameters. The measurement system makes multiple measurements along the length of the working lead when measuring multiple leads mounted on a carrier. By providing comprehensive on-line monitoring of the coating thickness while building the layers, more efficient and accurate dip coating of the working lead is achieved. The on-line manner includes making measurements after each impregnation to produce a layer during the impregnation process. In contrast, conventional methods typically make measurements after all the layers have been applied or formed on the lead. The present method adjusts the impregnation parameters based on the thickness measured after each impregnation and other factors being monitored, such as the temperature or viscosity of the impregnation solution (also referred to as the coating solution). In some embodiments, environmental factors can also be analyzed along with the measurements of the coated lead to adjust the impregnation parameters. The system and method can optimize the manufacturing process, such as by reducing the number of impregnations required to achieve a desired coating thickness within a target window.

[0034] In some embodiments, the measurement system provides the thickness of each membrane (such as the enzyme membrane and the glucose limiting membrane) of the working lead of the sensor based on the impregnation parameters and environmental factors. The impregnation parameters can include at least one of impregnation solution viscosity, impregnation solution temperature, immersion speed, dwell time, withdrawal speed, and air flow. The environmental factors can include air temperature, air flow speed, and relative humidity of the air flow. The impregnation parameters and environmental factors are monitored and tracked during the formation of the membranes.

[0035] Embodiments of the present disclosure create factory calibration by using the working wire diameter and sensor sensitivity of a sensor after forming an interference membrane, an enzyme membrane, and a glucose limiting membrane. The working wire diameter includes the enzyme membrane thickness and the glucose limiting membrane thickness. A correlation is determined between a first parameter of the enzyme membrane and a second parameter of the glucose limiting membrane and at least one of the factory sensitivity and drift characteristics of the sensor. The factory sensitivity or drift characteristics are associated with the sensor. Thus, the sensor outputs a glucose reading based on at least one of the factory sensitivity or drift characteristics when in use. By implementing this method, the need for a user to perform frequent local calibrations when using the sensor is eliminated, making the frequent finger prick method for obtaining in-use calibrated blood glucose measurements unnecessary. Thus, ease of use is greatly improved for patients while maintaining or improving the accuracy of the sensor readings.

[0036] Reference Figure 1 , shows a cross-sectional view of a working wire 100 according to some embodiments. In this example, the working wire 100 is an elongated wire having a circular cross-section. It should be understood that other cross-sections, such as square, rectangular, triangular, or other geometric shapes, may be used. Additionally, the working wire 100 may take other forms, such as a plate or a strip. The working wire serves as the working electrode of a continuous biosensor, such as the working electrode of a continuous glucose monitor.

[0037] In the example shown, the working wire 100 has a substrate 110 on which a biofilm 120 may be disposed. The types of biofilms that may be used are well known and will not be described in detail herein. In one example as shown, the biofilm 120 includes an interference membrane 121 (which may also be referred to as an interference layer) on the substrate 110, an enzyme membrane 122 (i.e., an enzyme layer) on the interference membrane 121, and a glucose limiting membrane 123 (i.e., a glucose limiting layer) on the enzyme membrane 122. In some embodiments, a protective or outer coating may optionally be applied over the glucose limiting membrane 123. Although the working wire 100 is shown as having three membranes 120, it should be understood that the number of membranes 120 may be more or less.

[0038] The substrate 110 may be composed of a core 113 having an outer layer 115. In Figure 1In the example, the core 113 is an elongated wire that is dense, malleable, very hard, easy to manufacture, has high thermal and electrical conductivity, and is also corrosion resistant. Example materials for the core 113 include tantalum, carbon, or a cobalt-chromium (Co-Cr) alloy. The core 113 may have an outer layer 115 such as platinum deposited or applied using an electroplating process. It should be understood that other processes may be used to apply the outer layer 115 to the core 113. For a glucose monitor, the platinum outer layer facilitates the reaction of hydrogen peroxide to produce water and hydrogen ions and a reaction that produces two electrons. The electrons are attracted into the platinum by a bias voltage placed across the platinum wire and the reference electrode. In this way, the magnitude of the current flowing in the platinum is intended to be related to the amount of hydrogen peroxide reaction, which in turn is proportional to the number of oxidized glucose molecules. Thus, the measured value of the current on the platinum wire can be correlated with the specific glucose level in the patient's blood or interstitial fluid (ISF).

[0039] The core 113, the outer layer 115, the interference membrane 121, and the enzyme membrane 122 form key aspects of the working wire 100. Additional layers or membranes may be introduced as needed depending on the specific biological substance being tested and the unique requirements of the application. In some cases, the core 113 may have an inner core portion (not shown). For example, if the substrate (core 113) is made of tantalum, an inner core of titanium or a titanium alloy may be included to provide additional strength and straightness.

[0040] In some cases, one or more membranes (i.e., layers) may be provided over the enzyme membrane 122. For example, a glucose limiting membrane 123 may be laminated on top of the enzyme membrane 122. The glucose limiting membrane 123 may limit the number of glucose molecules that can pass through the glucose limiting membrane 123 and into the enzyme membrane 122. The glucose limiting membrane 123 may be constructed as described in U.S. Patent No. 11,576,595, titled "Enhanced Sensor for a Continuous Biological Monitor," which is owned by the assignee of the present disclosure and is incorporated herein by reference in its entirety. In some cases, the addition of the glucose limiting membrane 123 has been shown to enable better performance of the entire working wire 100.

[0041] The interference film 121 is applied over the outer layer 115. The interference film 121 may be disposed between the enzyme film 122 and the outer layer 115. The interference film 121 is configured to completely wrap the outer layer 115 to protect the outer layer 115 from further oxidation. The interference film 121 is also configured to substantially restrict the passage of larger molecules, such as acetaminophen, to reduce contaminants that may reach the platinum and skew the results. Additionally, the interference film 121 may pass a controlled level of hydrogen peroxide (H2O2) from the enzyme film 122 to the platinum outer layer 115. The compositions for the interference film 121 and the enzyme film 122 may be as described in U.S. Patent Application No. 17 / 449,562, entitled "Working Wire for a Continuous Biological Sensor with an Immobilization Network," and U.S. Patent Application No. 17 / 449,380, entitled "In-Vivo Glucose Specific Sensor," which are owned by the assignee of the present disclosure and are incorporated herein by reference as if set forth in their entirety.

[0042] Figure 2 FIG. is a process flow diagram for manufacturing a working wire and a reference wire according to some embodiments. The specific steps, combinations of steps, and order of steps provided for this process are for illustrative purposes only. Other processes with different steps, combinations of steps, or order of steps may also be used to achieve the same or similar results. In some embodiments, features or functions described for one of the steps performed by one of the components may be enabled in different steps or components. Additionally, some steps may be performed before, after, or overlapping with other steps, regardless of the order of the steps shown. Further, some of the functions of this process (or alternatives to these functions) are described elsewhere herein. For the working wire, the process begins with the assignment of an identifier, such as a scannable code (e.g., barcode, quick response "QR" code, etc.), for tracking progress during manufacturing. At block 205, an uncoated bare wire (e.g., a raw material wire) is used, which may be a conductive wire made of, for example, platinum. At block 210, the wire is processed in a scraping and splitting station. Here, scraping and splitting are performed to shape the wire, which may include removing an insulating portion from the wire in the working area that will be used for glucose measurement. Data sampling is performed periodically after this process.

[0043] Figure 3Depicts an example of data sampling during scraping and segmentation in the manufacture of sensor wires. Histogram 300 shows the data distribution of the uniformity of the windows created by removing the insulation portion from the wires (scraping window size). Removing the insulation in a uniform manner achieves consistent performance between sensors. In this data sampling, data is collected for a sample size of 28 wires (labeled 302), where the target scraping window size 304 is 1.00 mm, and the lower specification limit (LSL) 306 and upper specification limit (USL) 308 based on six sigma standards are recorded.

[0044] Return Figure 2 , at block 215, ultrasonic and plasma processes can be applied to remove oxidation on the outer surface of the platinum wire to clean and prepare the surface of the wire for electropolymerization. Block 220 represents an electropolymerization (or electrodeposition) station. Electropolymerization is performed on the wire to form the interference film 121. This process provides a solid coating around the wire that is conformal and very consistent, with controllable and repeatable layer formation. The interference film 121 does not conduct electrons but will allow ions and hydrogen peroxide to pass through at a preselected rate. Additionally, the interference film 121 can be formulated to be permeation-selective for specific molecules. In one example, the interference film 121 is formulated and deposited in a manner that restricts the passage of reactive molecules that may degrade the conductive wire as contaminants or may interfere with the electrical detection and transmission process.

[0045] Advantageously, the interference film 121 precisely tunes the wide surface area for hydrogen peroxide molecules to reach the underlying wire. Additionally, the formulation of the interference film 121 can be customized to allow restricting or rejecting certain molecules from reaching the underlying layer, e.g., restricting or rejecting the passage of large molecules or specific target molecules.

[0046] The interference film 121 is a solid coating around the wire. The interference film 121 can be precisely coated or deposited on the wire in a manner that allows hydrogen peroxide to pass predictably and consistently. Additionally, the allowable interaction area between the hydrogen peroxide and the surface of the wire is significantly increased because the interaction can occur anywhere along the scraped portion of the wire. In this way, the interference film 121 is able to increase the level of interaction between hydrogen peroxide molecules in the surface of the wire, such that the generation of electrons is significantly amplified relative to the working electrodes of the prior art. The interference film 121 enables the sensor to operate at a higher electron current, reducing the sensor's sensitivity to noise and interference from contaminants, and further enabling the use of less complex and less precise electronics in the housing. In one example, the ability to operate at a higher electron flow allows the electronics of the sensor to use a standard operational amplifier (op-amp), rather than the expensive precision operational amplifiers required by prior art sensor systems. The resulting improved signal-to-noise ratio allows for simplified filtering and simplified calibration.

[0047] Figure 4 An example of data sampling after electropolymerization during the manufacture of a sensor wire is depicted according to some embodiments. Histogram 400 shows the data distribution of data sampling for 184 wires (labeled 402), recording the upper specification limit (USL) 408 for the HU (hydroxyurea) response according to six sigma standards. Hydroxyurea can be used as a known interferent as a surrogate for interference performance.

[0048] In some embodiments, the enzyme film and the glucose limiting film are formed on the wire by an impregnation process. Referring Figure 2 , the aqueous impregnation station in block 225 forms the enzyme film 122 on the wire, and at block 230, the robotic impregnation station forms the glucose limiting film 123 on the wire, or in some embodiments, the robotic impregnation station is also used to form the enzyme film 122. The impregnation process is described in U.S. Patent Application No. 17 / 659,267, entitled "Coating a Working Wire for a Continuous Biological Sensor", which is owned by the assignee of the present disclosure and is incorporated herein by reference in its entirety as if set forth in full.

[0049] Figure 5Is an isometric view of an impregnation station 500 having a fixture 510 and a bath 520 according to some embodiments. A plurality of working wires 505 are mounted into the fixture 510. The fixture 510 is a retainer, depicted as a block in this embodiment, for transporting the working wires 505 through an impregnation process during manufacturing. In this embodiment, the working wires 505 can be fixed into the fixture 510 and mounted in a single row, spaced apart and extending from the edge of the fixture 510 such that each working wire 505 can be individually measured from various angles. In other embodiments, the wires can be arranged in other ways, such as arranged in more than one row, aligned or staggered with each other, as long as there is sufficient space between the wires such that each wire can be individually measured. The fixture 510 can include an identifier, such as a scannable code 515 (e.g., bar code, quick response "QR" code, etc.), for tracking the progress of a particular fixture 510 during manufacturing.

[0050] The bath 520 contains an impregnation solution 525 (or coating solution). The working wires 505 are immersed into the impregnation solution 525 to produce a desired film on the wires. For example, the impregnation process can be used to produce an enzyme film 122 or a glucose limiting film 123. Each film may require several impregnations (i.e., multiple coating repetitions) to build up the desired thickness of the complete film. Using several impregnation layers to produce a film may be advantageous in reducing the occurrence of pinholes in the film as compared to producing the entire film thickness with a single impregnation.

[0051] The bath 520 can include one or more sensors 530 that monitor aspects of the impregnation solution, such as viscosity or impregnation solution temperature. The system can also include environmental sensors 535 to monitor aspects of the surrounding environment, such as air temperature, relative humidity, and air flow velocity. Embodiments of the present disclosure advantageously utilize these environmental sensors to provide input to a controller to adjust impregnation parameters during manufacturing. In this way, the controller automatically makes adjustments to address process variations that are extremely challenging to manage manually. For example, changes in the properties of the impregnation solution due to environmental factors during the manufacturing process can be advantageously compensated for in real time. Batch-to-batch variations in impregnation solution viscosity or solids content can further affect how environmental factors impact the impregnation solution. These effects can also be addressed by the present system and method.

[0052] The layer thickness formed during the impregnation process depends on several factors. According to embodiments of the present disclosure, example impregnation parameters and adjustments that can be made based on thickness measurements and other sensor information include the following:

[0053] Impregnation solution viscosity: A thicker impregnation solution provides a thicker layer per impregnation than a thinner impregnation solution. The impregnation solution has an initially known viscosity that can change over time based on temperature, mixing, and evaporation.

[0054] Temperature of the impregnation solution: A colder impregnation solution provides a thicker layer per impregnation compared to a hotter one. The impregnation solution has an initially known temperature which can change over time due to external temperature, mixing, and evaporation.

[0055] Immersion speed: Inserting the working wire into the impregnation solution at a slower rate will result in a thicker layer per impregnation than inserting it at a faster rate. Based on all expected parameters, an initial insertion speed is set, which can be changed for subsequent impregnations according to environmental conditions and actual thickness measurements.

[0056] Dwell time: The dwell time is the amount of time the working wire remains fully immersed in the impregnation solution. A longer dwell time will result in a thicker layer per impregnation compared to a shorter one. Based on all expected parameters, an initial dwell time is set, which can be changed for subsequent impregnations according to environmental conditions and actual thickness measurements.

[0057] Withdrawal speed: Removing the working wire from the impregnation solution at a slower rate will result in a thinner layer per impregnation than at a faster rate. Based on all expected parameters, an initial withdrawal speed is set, which can be changed for subsequent impregnations according to environmental conditions and actual thickness measurements.

[0058] Airflow: When the working wire emerges from the impregnation solution, increasing the airflow reduces the solvent evaporation time and improves the uniformity of the coating. Measurements of the airflow speed and / or relative humidity can be used to adjust the impregnation parameters.

[0059] Adjusting the parameters is automatically performed by a specially designed system / algorithm of U.S. Patent Application No. 17 / 659,267, thereby improving the manufacturing output by reducing defects and processing time. Parameters can be adjusted in real time in addition to between batches of the impregnation solution, such as during the impregnation process (e.g., between impregnations). In terms of the amount of coating deposited per impregnation, the impregnation process also tends to be non-linear in nature, making it difficult to predict the thickness of the impregnated layer. The interaction of the impregnation parameters with other factors such as environmental conditions is also complex.

[0060] Use a wire plan as a customized program in the controller to perform an initial impregnation and subsequent impregnations in a plurality of impregnation sequences according to the wire plan and adjustments made by the controller based on the application. In an embodiment, the thickness of the impregnation layer is measured as an in-line process (i.e., as the working wire travels through the impregnation process), and the impregnation parameters are adjusted as needed to achieve the desired thickness within a target window of a thickness set point and / or within a predefined number of impregnations. For example, the total thickness of a membrane (e.g., an enzyme membrane or a glucose-limiting membrane) may be desired to be from 4 microns to 25 microns, such as from 6 microns to 19 microns, where multiple coatings are applied through the impregnation process to form the total thickness. The target window for the desired set point thickness can be, for example, ±1 to ±3 microns of the set point thickness, such as ±2 microns.

[0061] Conventional techniques typically use a fixed draw speed and a preset number of impregnations. After all impregnations are completed, the wire is measured, and wires that do not meet the diameter thickness specifications are rejected. In contrast, the present system and method measure the wire during the process; i.e., after each impregnation. By using the layer thickness measurement as feedback, the impregnation parameters can be automatically adjusted before the next impregnation is performed, enabling the working wire to be accurately completed without affecting the processing time. For example, if the coating of the membrane is found to be thinner than expected, the impregnation parameters can be adjusted to produce a thicker layer during the next impregnation so that the total thickness of the membrane can be met without adding more impregnations than originally planned. In another example, if the coating is found to be closer to the final desired thickness than expected, the impregnation parameters can be adjusted to produce a thinner layer during the next impregnation to avoid exceeding the diameter specifications, which could result in rejected parts. Since the interaction between impregnation variables (e.g., environmental conditions, impregnation solution viscosity, immersion and draw speeds, batch variations) is inherently very complex and the tolerance requirements for layer thickness are extremely tight (e.g., within microns), achieving the required accuracy of control and adjustment is extremely difficult to do manually or with conventional techniques. The systems and methods of the present disclosure provide control of layer dimensions and adjustment of impregnation parameters that cannot be achieved with conventional techniques.

[0062] For already impregnated and cured working wires, an automated measurement system uses an in-line optical measurement tool (i.e., an optical measurement tool used during the manufacturing process) to measure the diameter of each wire to derive the coating thickness accumulated from the last impregnation cycle. The optical measurement tool can be, for example, an optical micrometer that uses a laser beam to measure dimensions in a non-contact manner. The micrometer detects the size of the working wire by measuring the shadow of an object within the laser beam path. Since the robot is adjustable on multiple axes and can be controlled very precisely, each working wire in the fixture can have its thickness measured along its entire length and at different angles around its entire circumference. In this way, the thickness of each working wire is measured at each impregnation for multiple longitudinal positions and angular rotations. In some embodiments, measurements can be made at more than one location along the length of the wire, and then the fixture can be rotated about the longitudinal axis of the wire so that the diameter is measured again along its length from different orientations.

[0063] Figure 6 Depicts an example of data sampling after forming an enzyme film according to some embodiments. Refer to Figure 2 , box 225 describes the data sampling. Histogram 600 shows the enzyme film coating thickness, where the target coating thickness 604 is 2.0 micrometers (μm). The data distribution is based on 225 wires (labeled 602), and it is recorded that according to the six sigma standard, the lower specification limit (LSL) 606 of the enzyme film is 1.5 micrometers (μm) coating thickness and the upper specification limit (USL) 608 is 2.5 micrometers (μm) coating thickness. The yield of samples within the specifications is 98%.

[0064] Figures 7A to 7C Depicts an example of data sampling after forming glucose-restricting films on the first, second, and third days according to some embodiments. Refer to Figure 2 , box 230 describes the data sampling. The sensors used in the data sampling are from three different manufacturing days, which are not necessarily consecutive days. Each histogram 700-A, 700-B, and 700-C shows the data distribution of the sample wires (the number of samples is labeled 702), and it is recorded that according to the six sigma standard, the upper specification limit (USL) 708 of the tip thickness is 165 micrometers (μm). Figures 7A to 7C The histograms 700-A, 700-B, and 700-C in

[0065] Refer to Figure 2, the frames 250 to 265 also manufacture reference wires for the sensor. The reference wires can be made of silver and have a silver chloride layer surrounded by an ion-restricting membrane that does not conduct electrons. Applying the ion-restricting membrane over the silver / silver chloride layer ideally controls the current sensitivity of the sensor by controlling the ion flow from the silver / silver chloride layer. In this way, the current sensitivity can be advantageously controlled and defined. As should be understood, this can also serve as an auxiliary method for controlling the sensor sensitivity by controlling the release of chloride from the electrode surface.

[0066] The process for making the reference wire begins by assigning an identifier or scannable code to the wire. The specific steps, combinations of steps, and order of steps provided for this process are for illustrative purposes only. Other processes with different steps, combinations of steps, or order of steps can also be used to achieve the same or similar results. In some embodiments, the features or functions described for one of the steps performed by a component can be enabled in different steps or components. Additionally, some steps can be performed before, after, or overlapping with other steps, regardless of the order of the steps shown. Furthermore, some of the functions of this process (or alternatives to these functions) have been described elsewhere herein. In some embodiments, the wire is made of silver. At block 255, a splitting station forms the wire. At block 260, a slurry impregnation station applies a silver chloride layer to the silver wire through a dip coating process. At block 265, a robotic impregnation station applies the ion-restricting membrane through a dip coating process.

[0067] After completing the working wire ( Figure 2 of frames 205 to 230) and the reference wire ( Figure 2 of frames 250 to 265), it should be understood that the working wire and the reference wire can be associated in several ways to form a sensor. For example, the working wire and the reference wire can be placed side by side, formed concentrically, wound into a twisted relationship, layered, or formed into any other known physical relationship of the working wire and the associated reference wire.

[0068] Referring Figure 2 , at block 240, a calibration checkpoint performs a calibration check (i.e., a "calibration check") on the sensor. For example, the linearity and factory sensitivity of each sensor can be determined. The calibration check data can be obtained non-destructively and typically corresponds to the signal output of the sensor as a function of the concentration of multiple input analytes. That is, in a calibration check test, the sensor is placed in a test solution and the corresponding output signal is measured.

[0069] Figure 7DFIG. 700-D shows the sensitivity of the electrical response of a prior art CGM sensor. FIG. 700-D has an X-axis representing the glucose level present in the user's body, which is typically measured in milligrams per deciliter (mg / dL). The Y-axis represents the amount of current flowing in the working wire (sensor current), which is typically measured in nanoamperes (nA). As shown in the sensitivity plot 700-D, three user responses are shown by three different dashed lines L1, L2, and L3, which are the user responses when the CGM sensor is implanted and actively used. These user responses can be from three different users, or can be from the same user at different times. As shown, although each user response is linear, each user response has a very different baseline - labeled B1, B2, and B3 for lines L1, L2, and L3 respectively. This baseline is the sensor current when the blood glucose level is zero, and represents the amount of sensor current attributable to noise or contaminant interference. This noise / contamination must be taken into account during the user-specific calibration process. The response of the sensor is typically linear and follows the following algebraic equation:

[0070] Y = AX + B (Equation 1),

[0071] where A (the slope of the line, rise / run) is the glucose sensitivity, and B is the baseline. Typically, the value "A" represents the sensitivity of the sensor to glucose, and the value "B" represents the specificity of the sensor to glucose. In some embodiments, the relationship between the factory sensitivity and the baseline is determined. The baseline is the current generated by the sensor when the blood glucose level is zero. In some embodiments, this relationship is linear and can be associated with the sensor. Prior art CGM sensors typically have a significantly high in vivo baseline, which is caused by in vivo interfering compounds such as acetaminophen, ascorbic acid, and uric acid.

[0072] Due to the precise control of the impregnation process during sensor fabrication, the embodiments herein enable the user response to be nearly the same in all cases, and the user response crosses the X-axis and Y-axis at near zero, which is referred to as the "intercept". Thus, the sensors disclosed herein have a zero or near-zero intercept and therefore do not require local user calibration, but can rely entirely on factory calibration before being shipped to the user. For example, when the actual in vivo glucose concentration in a patient is zero, the current generated in response to the patient's in vivo glucose concentration can be less than 0.2 nA. Additionally, due to the consistent user response of the sensors disclosed herein, the credibility and accuracy of the resulting glucose readings are increased.

[0073] Figure 8ADepicts the linearity statistics of a sensor for calibration checks according to some embodiments. By strictly limiting the amount of glucose that can reach the enzyme membrane, the linearity of the overall response is improved. Histogram 800-A according to Six Sigma shows that the methods and systems used herein achieve 0.99 in a consistent manner for sample sensors (sample quantity labeled 802), and the lower specification limit (LSL) 806 is recorded. Figure 8B Depicts the sensitivity statistics of a sensor during calibration checks according to some embodiments. Sensitivity (e.g., slope) is shown by Histogram 800-B according to Six Sigma by measuring the output as a function of analyte concentration and performing linear regression. The data shown in Histogram 800-B consists of a sample quantity of 182 sensors (labeled 802 in Figure 8B ), and the lower specification limit (LSL) 806 and the upper specification limit (USL) 808 are recorded.

[0074] Embodiments herein create factory calibration by utilizing several factors of the sensor, such as the factory sensitivity of the sensor, enzyme membrane thickness, glucose limiting membrane thickness, total sensor diameter, and drift characteristics, to develop a transfer function for the factory calibration scheme. These factors are measured individually for each sensor to perform 100% calibration checks and 100% inspections on each wire / sensor during manufacturing. Data can be monitored and tracked by the identifier of a specific sensor or a scannable code. For example, all data and parameters generated and collected at the stations represented in Figure 2 boxes 205 to 265 can be associated with an identifier such that the data used to manufacture each sensor is known. This can be saved and used for calculations, such as transfer functions or algorithms. This data can also be used to predict the in vivo performance of the sensor. Thus, each sensor is assigned its actual factory sensitivity measured at the factory, rather than being assigned a sensitivity based on population, lot, or batch values. Generally, in the art, sensors are calibrated in "lots" or "batches". For example, a sensor is a member of a lot (or batch) of sensors, where sensors from the same and / or different manufacturing lots are assigned the same calibration factor, despite variations due to manufacturing. The basis for assigning the calibration factor is only that the sensor is a member of the same lot or batch. In some cases, this calibration factor can be associated with the baseline sensitivity of the sensor compared to the in vivo sensitivity.

[0075] Through analysis, it has been determined according to the present disclosure that the sensor sensitivity can depend on the sensor (e.g., working wire) membrane thickness, but other subtle differences in the sensor can also affect the sensitivity. For example, two sensors can have the same sensitivity but different membrane thicknesses (e.g., enzyme membrane and / or glucose limiting membrane). In another example, two sensors can have the same sensitivity but different working wire diameters. In each example, although the sensors have the same sensitivity, they perform differently from each other in terms of, for example, response time or glucose reading output. Thus, the embodiments herein determine the calibration scheme not only based on sensitivity but also based on differences due to manufacturing variations (such as working wire diameter), where the working wire diameter includes the enzyme membrane thickness and the glucose limiting membrane thickness.

[0076] In the embodiments herein, the enzyme membrane thickness, the glucose limiting membrane thickness, and the total sensor diameter of each sensor are measured based on impregnation parameters during manufacturing. This data can be used for the calibration of the specific sensor to which the data pertains. In some embodiments, only the enzyme membrane thickness is used in the calibration, only the glucose limiting membrane thickness is used in the calibration, or the working wire diameter including the enzyme membrane thickness and the glucose limiting membrane thickness is used in the calibration. In prior art systems, due to the conventional processes used to form the membranes, the thickness of each membrane cannot be measured or determined.

[0077] As described herein, the membrane thickness requires extremely tight tolerances (e.g., within microns). There is a correlation between the membrane thickness (e.g., enzyme membrane thickness and / or glucose limiting membrane) and in vivo performance such as sensor sensitivity and sensitivity drift. Bench testing of the manufactured sensors (e.g., performing calibration checks) can be used to collect data such as the sensitivity of the sensors. Since the sensors are tracked and monitored with a scannable code, all relevant data for a particular sensor being manufactured is known and available. This data includes the membrane thickness, the working wire diameter, the sensor diameter, and the impregnation parameters used during the dip coating process of each membrane. During bench testing, the sensitivity of the sensor can be recorded within the first few hours, and then the data can be used to create a transfer function, which can be a polynomial, linear, logarithmic, or exponential or other type of function. Ideally, this is close to a 1:1 sensitivity. Thereby, sensitivity drift characteristics and algorithms are generated.

[0078] Since the thicknesses of the enzyme membrane and the glucose limiting membrane are measured and associated with the sensor, this data can be used to determine correlations or relationships. For example, there can be a correlation between the enzyme membrane thickness and the lifetime or the change in sensitivity over time. This can improve the algorithm used for calibration rather than only using sensitivity as a variable.

[0079] Through data collection performed in conjunction with the present disclosure, the drift characteristics of sensor samples show the in vitro sensitivity response of sensors with the same sensitivity but different measured membrane thicknesses. For example, for sensors with a membrane thickness within specifications and close to the lower specification limit (e.g., a thinner membrane thickness), the sensor performance drifts rapidly upward over time, such as starting on day 2 and continuing for several consecutive days. In another example, for sensors with a membrane thickness within specifications and close to the mid-specification limit, the sensor performance drift is flat over time, such as until day 10 and then gradually decays. In another example, for sensors with a membrane thickness within specifications and close to the upper specification limit (e.g., a thicker membrane thickness), the sensor performance drifts downward over time in a continuous, linear manner. Also, in these examples, all sensors have the same sensitivity. Other factors such as membrane thickness, a first impregnation parameter for one membrane, and a second impregnation parameter for another membrane can be used in factory calibration instead of only considering sensitivity. In other words, the membrane thickness has a range considered to be within the specification. By tracking the working wire diameter and / or the sensor diameter (including the membrane thickness), sensitivity drift or performance can be predicted based on the actual membrane thickness.

[0080] Drift characteristics can be automatically generated for each manufactured sensor based on the correlations between the parameters used in the dip coating of the enzyme membrane and the dip coating of the glucose limiting membrane and the working wire diameter and sensitivity. In some embodiments, the greater the slope of the sensor (e.g., the sensitivity of the sensor), the greater the drift rate of the sensor. Multiple drift characteristics can be embedded in an algorithm and stored in a computing platform (such as a cloud server). The algorithm can include other data such as prior knowledge or historical data developed in clinical studies, and data generated for the sensor during manufacturing, such as bench data, membrane thickness, impregnation parameters, etc. The platform stores all the data and the algorithm. The CGM system includes a sensor that collaborates with electronics in a small housing typically worn on the skin. The CGM system is described in U.S. Patent No. 11,471,081, entitled "Continuous Glucose Monitoring Device", which is owned by the assignee of the present disclosure and is incorporated herein by reference as if set forth in its entirety. The electronics in the CGM system include a microprocessor and a transmitter. The transmitter is configured to transmit data through communication technologies such as WiFi systems, wireless technologies, low power, cellular communication, satellite communication, etc. and combinations thereof. Devices such as smart phones, computers, routers, hubs, cellular network transceivers, etc. or combinations thereof receive the data.

[0081] An algorithm can be a series of calculations, and in some embodiments, there is one algorithm assigned to all sensors. In other embodiments, there can be multiple algorithms that use the same data set, such as three to six algorithms. One of the algorithms can be assigned to a sensor based on some of the data collected during the manufacturing process. For example, enzyme membrane thickness and sensitivity can predict which algorithms to use for a sensor to obtain optimal performance. Since a large amount of data is collected during the manufacturing process, more potential factors need to be considered for correlations to predict changes in sensor performance over time (e.g., drift). In contrast, it is known in the art that data such as membrane thickness and impregnation parameters are not collected, and only the sensitivity of a batch of sensors is determined, rather than each sensor. In the present embodiment, data for each sensor is collected during the manufacturing process, and all data is carried forward by each sensor through its identifier. This data is a potential use for determining an algorithm or a series of algorithms. For example, there can be six algorithms that use the same data set in an expected manner. One of the six algorithms is assigned to a sensor based on heuristic rules, such as when the data of the sensor is A + B, algorithm one is assigned, and when the data of the sensor is A + B + C, algorithm two is assigned. A, B, and C can be data collected during the manufacturing process, such as enzyme membrane thickness, glucose-limiting membrane thickness, impregnation parameters, baseline (sensor current when the blood glucose level is zero and represents the amount of sensor current attributed to noise or contaminant interference), or mathematical relationships or correlations derived from the data. This helps to improve the overall performance and in vivo prediction of the sensor.

[0082] Algorithms for the variation of sensor sensitivity over time can be developed as time-based systems, such as using time to create a series of functions, which can be polynomial, logarithmic, or any mathematical model. For example, for a time-based system, over time, such as 15 days, there can be three linear regions. The first region can be from the 1st day to the 2nd day of in vivo use, the second region can be from the 2nd day to the 10th day of in vivo use, and the third region can be from the 10th day to the 15th day of in vivo use. Linear equations are associated with each region based on time and predict the sensitivity of the sensor. The nominal seed value (e.g., baseline) can be the calibration check value on day 0 normalized to 1. As a specific example, the equation or linear function can be 0.88x for the first region, 0.01x + 1 for the second region, and -0.015x + 1 for the third region, where x is time. In this way, the drift characteristics are created by dividing the performance into time regions, and each time region predicts the sensitivity based on a mathematical model. In some embodiments, the mathematical model can be derived from historical data from enzyme membrane thickness and glucose-limiting membrane thickness. In some embodiments, the mathematical model can be derived from data historical data, bench data, in vivo studies, and field data from membrane thickness, impregnation parameters, etc. In another example, different algorithms can be used based on heuristics such as current range. In some embodiments, the algorithm can be a blood glucose range-based system rather than a time-based system that takes into account hypoglycemic and hyperglycemic states. For example, in the hypoglycemic state of the lowest current, the seed value (baseline) can be not cleared but used in the calculation.

[0083] In a conventional sensor system, algorithms for sensor calibration are developed using fixed values of sensor baseline, sensor sensitivity, and sensor drift rate. The fixed values are typically the midpoints of the ranges. By doing so, the sensor needs to be manufactured according to narrow specifications so that the sensor fits the algorithm. In contrast, in some embodiments of the present disclosure, algorithms are developed based on sensor baseline, sensor sensitivity, and sensor drift rate each developed from different transfer functions. Compared with the method of using fixed parameters, this enables a more accurate algorithm to be achieved. It also enables sensors with a wider range of specifications to be manufactured to accommodate different ranges of baseline, sensitivity, and drift while accommodating the algorithm, thereby increasing the yield of sensors within the specifications during the manufacturing process.

[0084] Figure 8C FIG. 800-C is a graph of the baseline of a sensor and the sensitivity of the sensor according to some embodiments. There may be a relationship between the baseline current of the sensor and the in vivo current independent of glucose, such as:

[0085] bg = m x isig + b (Equation 2),

[0086] Wherein:

[0087] m = -0.00018026x b + 0.032465 (Equation 3),

[0088] Isig is the current, b is the baseline, bg is the background, where the background refers to any in vivo current that is not related to glucose, and m is the slope. Figure 800-C shows that there is no clear correlation between the quantities. For example, the data points do not have a tight fit to the proposed linear correlation.

[0089] Figure 8D is a plot 800-D of the logarithm of the baseline of the sensor and the logarithm of the sensitivity of the sensor from the data in Figure 8C Unexpectedly, it was found that when the logarithm of the sensor baseline and the logarithm of the sensor sensitivity are plotted on a graph, there is a predictable linear relationship between the current and the background, which can be used in a sensor algorithm (e.g., sensor calibration) to predict the behavior of the sensor. For example, in the field, the baseline of the sensor is known from the manufacturing history of the sensor (e.g., layer thickness). The current isig can be measured in the field, and then the correlation can be used to predict how much of the current is due to background noise rather than glucose. In one example, the relationship can be:

[0090] In(bg) = m x In(isig) + b (Equation 4),

[0091] Wherein:

[0092] m = -0.11653x b + 0.59964 (Equation 5).

[0093] This can be used to generate a factory calibration of the sensor and is influential for "marginal cases" such as hypoglycemia or for the early stage of sensor use such as the first to the third day. For example, the processor can determine the baseline of the sensor based on the enzyme membrane thickness and the glucose limiting membrane thickness. The baseline is the current generated by the sensor when the blood glucose level is zero. The processor can determine the correlation between the baseline of the sensor and the sensitivity. In some embodiments, the correlation can be a log-log relationship. The processor can use the correlation to determine the background current of the sensor. The background current is the current that is not related to glucose.

[0094] Figure 8E is a plot of the in vivo baseline and the in vivo sensitivity according to some embodiments. For the baseline and sensitivity of the sensor, a linear transfer function was found. The figure shows the sensitivity of the sensor on the x-axis and the baseline of the sensor on the y-axis, and the correlation can be linear, such as:

[0095] y = -99.797x + 2.8588 (Equation 6),

[0096] and R 2 = 0.9075, indicating a tight fit of the data points to this linear correlation. The linear correlation can be used as a transfer function to predict how the baseline changes as a function of sensitivity. Figure 8F is Graph 800-F of in vivo sensitivity drift and in vivo sensitivity according to some embodiments. For the drift and sensitivity of the sensor, a linear transfer function is generated. Graph 800-F shows an example of the sensitivity 820 of the sensor on the x-axis and the sensitivity drift rate (e.g., drift characteristic) 825 of the sensor on the y-axis. The correlation can be linear, such as:

[0097] y = -0.251x + 0.0086 (Equation 7),

[0098] which can be used as a transfer function to predict how sensitivity drifts over time for different sensitivity values. For example, historical data of in vivo sensitivity drift of various test samples can be used, and the relationship between the in vivo sensitivity of multiple sensors and the historical data of the drift characteristics of the sensors can be determined. In some embodiments, the relationship can be linear and can be associated with the sensor. The relationship can be part of an algorithm for predicting the sensitivity of the sensor over time. For example, during a patient's use of the sensor, the sensor can output a glucose reading based on the relationship. Figures 8C to 8F The correlation and transfer function are unique insights recognized in connection with the present disclosure, which are achieved due to detailed membrane thickness measurements and 100% tracking of individual sensor data.

[0099] In some embodiments, the processor of the platform is configured to communicate with the CGM system and can receive from the transmitter in the CGM system an acknowledgement that the sensor in the CGM system is being actively used by the patient. The processor can identify the drift characteristics, relationships, correlations, and all relevant data (e.g., membrane thickness) associated with the identifier of the sensor. Thereby, the processor can perform an appropriate algorithm based on the drift characteristics to compensate for the sensitivity drift of the sensor. Thus, the blood glucose reading or output of the sensor can be based on, for example, the drift characteristics or other data associated with the sensor. This occurs automatically without user input, thereby achieving a highly accurate performance of the sensor. By using this method of the processor communicating with the sensor and applying an algorithm to automatically adjust the output of the sensor, the need for the user to recalibrate the sensor by using a fingerstick method and obtaining a blood glucose reading is eliminated. In an embodiment, the platform and the CGM system are also connected and communicate with an electronic device such as a mobile phone or a computing tablet through an application.

[0100] Figure 9AFIG. 900 is a flow chart of a method for factory calibration of a sensor for a continuous glucose monitoring (CGM) system according to some embodiments. The specific steps, combinations of steps, and order of steps provided for this process are for illustrative purposes only. Other processes with different steps, combinations of steps, or order of steps may also be used to achieve the same or similar results. In some embodiments, features or functions described for one of the steps performed by one of the components may be enabled in different steps or components. Additionally, some steps may be performed before, after, or overlapping with other steps, regardless of the order of the steps shown. Further, some functions of this process (or alternatives to these functions) have been described elsewhere herein.

[0101] At block 902, a processor receives enzyme membrane data that includes the enzyme membrane thickness after each impregnation of a working wire in a first impregnation solution according to a first parameter for forming an enzyme membrane on the working wire. At block 904, the processor receives glucose limiting membrane data that includes the glucose limiting membrane thickness after each impregnation of the working wire in a second impregnation solution according to a second parameter for forming a glucose limiting membrane on the working wire. At block 906, the processor determines the working wire diameter after the working wire is formed. The working wire diameter includes the enzyme membrane thickness and the glucose limiting membrane thickness. The formed working wire includes an interference membrane, an enzyme membrane, and a glucose limiting membrane. At block 908, the processor automatically generates a correlation between the first parameter and the second parameter and at least one of the factory sensitivity and the drift characteristics of the sensor. The drift characteristics predict the sensitivity of the sensor over time. At block 910, the processor associates at least one of the factory sensitivity or the drift characteristics with the sensor. The sensor outputs a glucose reading based on at least one of the factory sensitivity or the drift characteristics during in vivo use.

[0102] Figure 9B FIG. 920 is a flow chart of a method for factory calibration of a sensor for a continuous glucose monitoring (CGM) system according to some embodiments. The specific steps, combinations of steps, and order of steps provided for this process are for illustrative purposes only. Other processes with different steps, combinations of steps, or order of steps may also be used to achieve the same or similar results. In some embodiments, features or functions described for one of the steps performed by one of the components may be enabled in different steps or components. Additionally, some steps may be performed before, after, or overlapping with other steps, regardless of the order of the steps shown. Further, some functions of this process (or alternatives to these functions) have been described elsewhere herein.

[0103] At block 922, the working wire is immersed in a first coating solution according to a first parameter to form an enzyme film on the working wire. At block 924, enzyme film data including the enzyme film thickness is measured. At block 926, the working wire is immersed in a second coating solution according to a second parameter to form a glucose limiting film on the wire. At block 928, glucose limiting film data including the glucose limiting film thickness is measured. At block 930, the processor determines the working wire diameter after the working wire is formed. The working wire diameter includes the enzyme film thickness and the glucose limiting film thickness. The formed working wire includes an interference film, an enzyme film, and a glucose limiting film. At block 932, the processor that communicates with the CGM system automatically generates a correlation between at least one of the first parameter and the second parameter and the factory sensitivity and the drift characteristics of the sensor. The drift characteristics predict the sensitivity of the sensor over time. At block 934, the processor associates at least one of the factory sensitivity or the drift characteristics with the sensor. The sensor outputs a glucose reading based on at least one of the factory sensitivity or the drift characteristics during in vivo use.

[0104] In a non-limiting example, the average sensitivity of a known sensor is 25 picoamps / mg / dL. When the sensor sensitivity is 40 picoamps / mg / dL, the minimum current can be 1,000 picoamps, and when the sensor sensitivity is measured at 400, the maximum current can be 10,000 picoamps. During the first day of a patient using the sensor, monitoring the current can show that the rolling current average is 80% of the maximum value over a continuous time period. Thus, it can be determined that the sensor is a high-sensitivity sensor, and a high-sensitivity projection for the drift characteristics is automatically assigned to the sensor without user input.

[0105] Figure 10 is a schematic diagram of a CGM system being used by a patient according to some embodiments. Patient 1002 (e.g., the user) can subcutaneously insert the sensor 1004 of the CGM system into their body for use. The electronics 1006 of the CGM system communicate with a computing platform 1008 such as a cloud server. The computing platform 1008 communicates with a manufacturing facility 1010. In this way, the manufacturing facility 1010 provides data and information to the computing platform 1008, such as data and information for sensitivity values and drift characteristics. The drift characteristics can be generated by the computing platform 1008 or by the manufacturing facility 1010 and stored in the computing platform 1008. The computing platform 1008 communicates with the electronics 1006 of the CGM system to provide drift characteristics to compensate for the sensitivity of the sensor 1004. The communication can be through communication technologies such as WiFi systems, wireless technologies, low-power, cellular communication, satellite communication, etc. and combinations thereof.

[0106] Figures 11A to 11CIt is a table of detailed process improvements according to some embodiments. Figure 11A It shows process improvements aimed at reducing the standard deviation achieved over a three - month period. By improving the enzyme thickness distribution 1105, the standard deviation 1110 in millimeters improved from 0.0036 to 0.0011. Figure 11B It shows process improvements to the glucose - limiting membrane thickness 1125 by adjusting the glucose - limiting membrane formulation and impregnation profile. The improvement was recorded by reducing the standard deviation 1130 from 0.80 to 0.55. Additionally, the yield 1135 increased from 79.5% to 92.1%. Figure 11C It shows a production calibration summary comparison. During the calibration check, the sensitivity distribution 1145 improved significantly as indicated in the sensitivity standard 1150. The sensitivity standard 1150 during the calibration check changed from 55 ± 10 in January to 30 ± 10 in April, resulting in a 34% increase in process capability 1155.

[0107] Figure 12 It shows an automated factory software control process according to some embodiments. The specific steps, step combinations, and step sequences provided for this process are for illustrative purposes only. Other processes with different steps, step combinations, or step sequences can also be used to achieve the same or similar results. In some embodiments, the features or functions described for one of the steps performed by one of the components can be enabled in different steps or components. Additionally, some steps can be performed before, after, or overlapping with other steps, regardless of the order of the steps shown. Furthermore, some of the functions of this process (or alternatives to these functions) have been described elsewhere. Process 1200 starts at the dashboard of the process at block 1205. The dashboard can be implemented on the device and is a graphical user interface (GUI) that provides a visual representation of information, data, metrics, etc. The dashboard monitors and manages various aspects of the systems and methods described herein. At block 1210, wires (e.g., the working wire and reference wire of the sensor) start at the scraping and splitting station as described in reference Figure 2 , blocks 210 and 215. Data can be displayed when the wires are positioned in the fixture at block 1215 and used for inspection at block 1220. To form the working wire, the wire advances to block 1225, and the software monitors parameters when the working wire enters the electro - polymerization station to form the interference film (as described in reference Figure 2 , block 220). At blocks 1230 and 1235, the software is used to control the formation of the enzyme membrane and the glucose - limiting membrane (as described in Figure 2 , blocks 225 and 230 respectively).

[0108] To form the reference wire, after the inspection station at block 1220, the reference wire, in some embodiments, a third party may have applied a silver / silver - chloride coating at a specified thickness (Figure 2 of the frame 260). Then, the wire advances to frame 1240, where an ion confinement membrane is formed by a dip coating process (as described in Figure 2 frame 265). The working wire and the reference wire advance to frame 1245, where a calibration check is performed on the wires and further data may be recorded. At frame 1250, the working wire and the reference wire are assembled to form a sensor, and as described in frame 1245, a calibration check is performed on the sensor and further data may be collected. At frame 1255, the sensor is programmed and tests may be performed. At frame 1260, the sensor is encapsulated for sale. During the calibration check and throughout the process, the wire or the sensor may be marked as out of specification. These sensors are isolated and sent to frame 1265, the non-conformance station. At this station, if possible, the sensor may be scratched or repaired and returned to the process. At frame 270, the fixtures may be cleaned and all data is stored in a repository.

[0109] Figure 13 is a simplified schematic diagram showing an example computer system 1300 (representing any combination of one or more of the computer systems) for use in a computing platform 1008 to execute any of the programs described herein. Other embodiments may use other components and combinations of components. For example, depending on the complexity of the computing platform 1008, the computer system 1300 may represent one or more physical computer devices or servers, such as a web server, a rack-mounted computer, a network storage device, a desktop computer, a laptop / notebook computer, etc. In some embodiments implemented at least partially in a cloud network potentially having data synchronized across multiple geographical locations, the server 1300 may be referred to as one or more cloud servers. In some embodiments, the functions of the server 1300 are enabled in a single computer device. In more complex implementations, whether in a single server farm facility or in multiple physical locations, some functions of the computing system are distributed across multiple computer devices. In some embodiments, the server 1300 serves as a single virtual machine.

[0110] In some embodiments where the server 1300 represents multiple computer devices, some functions of the server 1300 are implemented in some computer devices while other functions are implemented in other computer devices. In the illustrated embodiment, the server 1300 generally includes at least one processor 1302, a main electronic memory 1304, a data storage device 1306, a user I / O 1308, and a network I / O 1310 connected or coupled together by a data communication subsystem 1312, and other components not shown for simplicity.

[0111] Processor 1302 represents one or more central processing units on one or more printed circuit boards (PCBs) within one or more enclosures or housings. In some embodiments, processor 1302 represents multiple microprocessor units in multiple computer devices at multiple physical locations interconnected by one or more data channels. When cooperating with main electronic memory 1304 to execute computer-executable instructions for performing the above-described functions of server 1300, processor 1302 becomes a special-purpose computer for performing the functions of the instructions.

[0112] Main electronic memory 1304 represents one or more RAM modules on one or more PCBs within one or more enclosures or housings. In some embodiments, main electronic memory 1304 represents multiple memory module units in multiple computer devices at multiple physical locations. When operating with processor 1302, main electronic memory 1304 stores computer-executable instructions executed by processor 1302 and data processed or generated by that processor to perform the above-described functions of server 1300.

[0113] Data storage device 1306 represents or includes any suitable number or combination of internal or external physical mass storage devices, such as hard disk drives, optical drives, network-attached storage (NAS) devices, flash drives, etc. In some embodiments, data storage device 1306 represents multiple mass storage devices in multiple computer devices at multiple physical locations. Data storage device 1306 generally provides persistent storage (e.g., in non-transitory computer-readable medium or machine-readable medium 1314) for programs (e.g., computer-executable instructions) and data used in the operation of processor 1302 and main electronic memory 1304.

[0114] In some embodiments, main electronic memory 1304 and data storage device 1306 include all or part of the programs and data (e.g., represented by 1320 to 1380) required for processor 1302 to execute the methods, processes, and functions disclosed herein (e.g., in Figures 1 to 12 ). Under the control of these programs and using this data, processor 1302 cooperates with main electronic memory 1304 to perform the above-described functions for computing platform 1008.

[0115] User I / O 1308 represents one or more suitable user interface devices, such as keyboards, pointing devices, displays, etc. In some embodiments, user I / O 1308 represents multiple user interface devices for multiple computer devices at multiple physical locations. For example, a system administrator can use these devices to access, set up, and control server 1300.

[0116] Network I / O 1310 represents any suitable networking device for communicating via computing platform 1008, such as a network adapter and the like. In some embodiments, network I / O 1310 represents multiple such networking devices for multiple computer devices at multiple physical locations for communicating via multiple data channels.

[0117] Data communication subsystem 1312 represents any suitable communication hardware for connecting other components either in a single unit or in a distributed manner on one or more PCBs, within one or more enclosures or enclosures, within one or more rack assemblies, within one or more geographical locations, etc.

[0118] Server 1300 includes a memory storing executable instructions (loaded from data storage device 1306) and a processor 1302. Processor 1302 is coupled to memory 1304 and executes methods by executing instructions stored in memory 1304. Non - transitory computer - readable medium 1314 includes instructions that, when executed by processor 1302, cause processor 1302 to perform operations including methods 900 and 920 as described herein.

[0119] Reference has been made in detail to the embodiments of the disclosed invention, and one or more examples of the embodiments have been shown in the accompanying drawings. Each example is provided by way of explanation of the technology and not limitation thereof. In fact, although this specification has been described in detail with reference to specific embodiments of the invention, it should be understood that those skilled in the art can readily conceive of alternatives, variations, and equivalents of these embodiments after understanding the foregoing. For example, features shown or described as part of one embodiment can be used with another embodiment to yield yet another additional embodiment. Accordingly, it is intended that this subject matter cover all such modifications and variations within the scope of the appended claims and their equivalents. Without departing from the scope of the invention, those of ordinary skill in the art can make these and other modifications and changes to the invention, the scope of which is set forth more specifically in the appended claims. In addition, those of ordinary skill in the art will understand that the foregoing description is merely exemplary and is not intended to limit the invention.

Claims

1. A method for factory calibration of a sensor for a continuous glucose monitoring (CGM) system, the method comprising: Receiving, by a processor, enzyme membrane data, the enzyme membrane data including the enzyme membrane thickness after each immersion of the working wire in a first immersion solution according to a first parameter for forming an enzyme membrane on the working wire; Receiving, by the processor, glucose limiting membrane data, the glucose limiting membrane data including the glucose limiting membrane thickness after each immersion of the working wire in a second immersion solution according to a second parameter for forming a glucose limiting membrane on the working wire; Determining, by the processor, a working wire diameter after the working wire is formed, wherein the working wire diameter includes the enzyme membrane thickness and the glucose limiting membrane thickness, and wherein the formed working wire includes an interference membrane, the enzyme membrane, and the glucose limiting membrane; Automatically generating, by the processor in communication with the CGM system, a correlation between the first parameter and the second parameter and at least one of i) factory sensitivity and ii) drift characteristics of the sensor, wherein the drift characteristics predict the sensitivity of the sensor over time; And Associating, by the processor, at least one of the factory sensitivity or the drift characteristics with the sensor, wherein the sensor outputs a glucose reading based on at least one of the factory sensitivity or the drift characteristics during in vivo use.

2. The method of claim 1, wherein the drift characteristics are divided into time regions, and each time region predicts the sensitivity based on a mathematical model.

3. The method of claim 2, wherein the mathematical model is derived from historical data of enzyme membrane thickness and glucose limiting membrane thickness.

4. The method of claim 1, the method further comprising: Determining, by the processor, a relationship between the factory sensitivity and a baseline, wherein the baseline is a current generated by the sensor when the blood glucose level is zero; And Associating, by the processor, the relationship with the sensor.

5. The method of claim 4, wherein the relationship is linear.

6. The method of claim 1, the method further comprising: Determining, by the processor, a relationship between the in vivo sensitivity of a plurality of sensors and historical data of the drift characteristics of the sensor; And Associating, by the processor, the relationship with the sensor.

7. The method of claim 6, wherein the relationship is linear.

8. The method of claim 1, the method further comprising: Determining, by the processor, a baseline of the sensor based on the enzyme membrane thickness and the glucose limiting membrane thickness, wherein the baseline is a current generated by the sensor when the blood glucose level is zero; Determining, by the processor, a correlation between the baseline of the sensor and the sensitivity, the correlation being a log-log relationship; And Using, by the processor, the correlation to determine a background current of the sensor, wherein the background current is a current independent of glucose.

9. The method of claim 1, the method further comprising: The processor receives from the transmitter in the CGM system confirmation that the sensor in the CGM system is being used by a patient; The processor identifies the drift characteristics of the sensor; and The processor transmits a new sensitivity of the sensor to a microprocessor of the CGM system based on the drift characteristics of the sensor, wherein the sensor outputs a glucose reading based on the drift characteristics.

10. The method according to claim 1, wherein the interference film is formed by an electro-polymerization process.

11. The method according to claim 1, the method further comprising measuring interference film data including interference film thickness after forming the interference film on the working wire.

12. The method according to claim 1, wherein the first parameter and the second parameter include at least one of impregnation solution viscosity, impregnation solution temperature, immersion speed, residence time, withdrawal speed, and air flow.

13. The method according to claim 1, wherein each impregnation of the working wire comprises: Impregnating the working wire into a first impregnation solution according to the first parameter for forming the enzyme film, or impregnating the working wire into a second impregnation solution according to the second parameter for forming the glucose limiting film; As an on-line process, measuring a plurality of diameters along the length of the working wire using an automated measurement system; Determining a thickness difference by the processor communicating with the automated measurement system, the thickness difference being the difference between a thickness set point and an aggregated standard of the plurality of diameters; and Calculating an adjusted parameter for the impregnation process by the processor based on the thickness difference.

14. A method for factory calibration of a sensor for a continuous glucose monitoring (CGM) system, the method comprising: Impregnating a working wire in a first coating solution according to a first parameter to form an enzyme film on the working wire; Measuring enzyme film data, wherein the enzyme film data includes enzyme film thickness; Impregnating the working wire in a second coating solution according to a second parameter to form a glucose limiting film on the working wire; Measuring glucose limiting film data, wherein the glucose limiting film data includes glucose limiting film thickness; Determining a working wire diameter by the processor after the working wire is formed, wherein the working wire diameter includes the enzyme film thickness and the glucose limiting film thickness, and wherein the formed working wire includes an interference film, the enzyme film, and the glucose limiting film; Automatically generating, by the processor communicating with the CGM system, a correlation between the first parameter and the second parameter and at least one of i) factory sensitivity and ii) drift characteristics of the sensor, wherein the drift characteristics predict the sensitivity of the sensor over time; and Associating, by the processor, at least one of the factory sensitivity or the drift characteristics with the sensor, wherein the sensor outputs a glucose reading based on at least one of the factory sensitivity or the drift characteristics during in vivo use.

15. The method according to claim 14, wherein the drift characteristics are divided into time regions, and each time region predicts the sensitivity based on a mathematical model.

16. The method according to claim 15, wherein the mathematical model is derived from historical data from the enzyme membrane thickness and the glucose-limiting membrane thickness.

17. The method according to claim 14, the method further comprising: determining, by the processor, a relationship between the factory sensitivity and a baseline, wherein the baseline is the current generated by the sensor when the blood glucose level is zero; and associating, by the processor, the relationship with the sensor.

18. The method according to claim 14, the method further comprising: determining, by the processor, a relationship between the in-vivo sensitivity of a plurality of sensors and historical data of the drift characteristics of the sensor; and associating, by the processor, the relationship with the sensor.

19. The method according to claim 14, the method further comprising: determining, by the processor, a baseline of the sensor based on the enzyme membrane thickness and the glucose-limiting membrane thickness, wherein the baseline is the current generated by the sensor when the blood glucose level is zero; determining, by the processor, a correlation between the baseline of the sensor and the sensitivity, the correlation being a log-log relationship; and determining, by the processor, a background current of the sensor using the correlation, wherein the background current is a current independent of glucose.

20. The method according to claim 14, the method further comprising: receiving, by the processor, from a transmitter in the CGM system a confirmation that the sensor of the CGM system is being used by a patient; identifying, by the processor, the drift characteristics of the sensor; and transmitting, by the processor, a new sensitivity of the sensor to a microprocessor of the CGM system based on the drift characteristics of the sensor, wherein the sensor outputs a glucose reading based on the drift characteristics.

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