Wireless sensing method based on Hamiltonian jumping point following

Abstract

The present application belongs to the field of sensor technology, and relates to a wireless sensing method based on Hamiltonian jump point following, including: establishing an oscillation model of a wireless sensor; the oscillation model includes: a first oscillator, a second oscillator, a loss device and a gain device, the first oscillator and the second oscillator are arranged at intervals, the loss device is connected in parallel with the first oscillator, and the gain device is connected in parallel with the second oscillator; when the first oscillator and the second oscillator are coupled through mutual inductance, the resistance values ​​of the first oscillator and the loss device are changed, and a Riemann surface is formed by clockwise and counterclockwise rotation; when a Hamiltonian jump point is generated on the Riemann surface, a pair of singular points are obtained; wireless sensing is performed according to the singular points to improve the responsiveness of the wireless sensor. The use of the present application can greatly improve the responsiveness of the LC sensor.

Description

Technical Field

[0001] The present application relates to the field of sensor technology, and in particular to a wireless sensing method based on Hamiltonian jumping point following. Background Art

[0002] Inductive-capacitive (LC) sensors are widely used in industrial and biomedical fields. They can convert target signals into changes in the resonant frequency of an external wirelessly coupled reader, eliminating the need for built-in batteries and integrated chips. Today, the development of LC sensors has enabled some miniaturized devices to be widely used, especially in biomedical implants for measuring intraocular pressure, intracranial pressure, pulmonary artery pressure, etc.

[0003] However, these LC sensors often suffer from low responsivity, characterized by a resonant frequency shift in the reader's reflected spectrum. Due to the weak responsivity of miniaturized devices and the low Q-factor resonance caused by high magnetic field absorption in the surrounding environment, small capacitive perturbations of LC sensors cannot produce a significant spectral response. These challenges severely limit their widespread application in industrial miniaturized sensors and biomedical implants. Therefore, improving the responsivity of LC sensors is of great significance to expand the application of LC sensors in industrial and biomedical fields.

[0004] At present, there are quite a lot of papers and technical solutions for improving the responsiveness of LC sensors at home and abroad, but the technical means are mainly concentrated on introducing new sensor designs, optimizing sensor materials, improving signal processing algorithms, etc. Their effect on improving responsiveness is not significant, and only improves it by up to ten times. Summary of the invention

[0005] Based on this, it is necessary to provide a wireless sensing method based on Hamiltonian jumping point following to address the above technical problems, which can greatly improve the responsiveness of LC sensors.

[0006] The wireless sensing method based on Hamiltonian jumping point following includes:

[0007] Establishing an oscillation model of the wireless sensor; the oscillation model comprises: a first oscillator, a second oscillator, an amplifier and a loss device, the first oscillator and the second oscillator are arranged at intervals, the amplifier is connected in parallel with the first oscillator, and the loss device is connected in parallel with the second oscillator;

[0008] When the first oscillator and the second oscillator are coupled through mutual inductance, the resistance values ​​of the second oscillator and the loss device are changed, and a Riemann surface is formed by clockwise and counterclockwise rotation;

[0009] When a Hamiltonian jump point is generated on the Riemann surface, a pair of singular points is obtained;

[0010] Wireless sensing is performed based on the singular point to improve the responsiveness of the wireless sensor.

[0011] In one embodiment, the method further includes: reducing the distance between the first oscillator and the second oscillator to increase the coupling strength between the first oscillator and the second oscillator and reduce noise.

[0012] In one embodiment, the method further includes: increasing the resistance of the loss device to reduce the loss rate and the noise.

[0013] In one embodiment, the method further includes: reducing the change rate of the resistance value in the second oscillator to reduce noise.

[0014] In one embodiment, the first oscillator is a parallel resonant circuit, comprising: a first inductor and a first capacitor;

[0015] The first inductor, the first capacitor, and the booster are connected in parallel.

[0016] In one embodiment, the second oscillator is a parallel resonant circuit, comprising: a second inductor and a second capacitor;

[0017] The second inductor, the second capacitor, and the loss device are connected in parallel.

[0018] In one embodiment, changing the resistance of the second oscillator and the loss device includes: changing the resistance of the second capacitor of the second oscillator and the loss device.

[0019] In one embodiment, both the first capacitor and the second capacitor are programmable capacitors, and the first inductor and the second inductor are tuned to corresponding resonant frequencies using the first capacitor and the second capacitor, respectively.

[0020] In one embodiment, the gain controller is a negative impedance converter.

[0021] In one embodiment, the dissipator is a programmable resistor.

[0022] The wireless sensing method based on Hamiltonian jump point following introduces a nonlinear term, saturation gain, which leads to the emergence of a pair of HHPs. By tracking the Hamiltonian jump caused by non-adiabatic transitions during dynamic surround, high-sensitivity detection of sensor disturbances is achieved. The Hamiltonian jump is nonlinearly split under small disturbances, resulting in the simultaneous degradation of eigenvalues ​​and eigenvectors, thereby generating an amplified characteristic frequency response, enhancing the frequency response of the system, and achieving a significant enhancement of the frequency response at the Hamiltonian jump point. Furthermore, the sensor's responsiveness can be further improved by adjusting the system parameters, especially the coupling strength and loss. In addition, the noise analysis at the HHP was performed using Monte Carlo simulation, and the noise level was reduced by operating the system in a strong coupling region and slowly adjusting the system parameters through time evolution. This application utilizes EPs to improve the responsiveness of the LC sensor to a greater extent and overcome the noise of the sensor at the singular point. The principle is clear and the application range is wide. It provides new ideas for the development of sensor technology and can play an important role in the industrial and biomedical fields, such as: optical microcavity sensors, biophotonic sensors, microscale pressure sensors and other equipment, especially suitable for biomedical implants and other fields, such as: measuring intraocular pressure, intracranial pressure, pulmonary artery pressure and other biomedical implants. In addition, it also has great potential in the fields of photonics and electronics and can be used for sensitive biomolecule detection and physiological status monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic diagram of a flow chart of a wireless sensing method based on Hamiltonian jump point following in one embodiment;

[0024] Figure 2 is a circuit diagram of an oscillation model of a wireless sensor in one embodiment;

[0025] Figure 3 A physical diagram of an oscillation model of a wireless sensor in one embodiment;

[0026] Figure 4 is a counterclockwise rotating Riemann surface in one embodiment;

[0027] Figure 5 is a Riemann surface rotating clockwise in one embodiment;

[0028] Figure 6 is the resonant frequency in the parameter space of R2 and C2 in one embodiment;

[0029] Figure 7 : is the frequency response under different disturbances (step 3.8 pF) when scanning C2 in counterclockwise and clockwise directions in one embodiment;

[0030] Figure 8is the linear dependence of HHP on sensor disturbance in one embodiment;

[0031] Fig. 9 is a schematic diagram of an EP system in one embodiment;

[0032] Fig.10 is the characteristic frequency of the EP system in one embodiment;

[0033] Fig.11 The characteristic frequency solution surface and the dynamic surround of EP in a two-dimensional parameter space in one embodiment;

[0034] Fig.12 is a cross section of a Riemann surface when κ=0.05 in one embodiment;

[0035] Fig.13 A wireless sensing mechanism in one embodiment;

[0036] Fig.14 is a linear relationship between sensor capacitance perturbation and HHP offset in one embodiment;

[0037] Fig.15 is the relationship between the responsiveness enhancement factor at HHP and the frequency change of the standard method in one embodiment;

[0038] Fig.16 is a numerical result of characteristic frequency when the coupling ratio κ / γ=1.25 in one embodiment;

[0039] Fig.17 is a numerical result of characteristic frequency when the coupling ratio κ / γ=1.5 in one embodiment;

[0040] Fig.18 The experimental results in the strong coupling region are obtained by quickly (200 milliseconds) scanning the programmable capacitor C2 in the front-back direction in one embodiment;

[0041] Fig.19 The experimental results in the strong coupling region are obtained by slowly (500 milliseconds) scanning the programmable capacitor C2 in the front-to-back direction in one embodiment;

[0042] Fig. 20 The following are characteristic test results of a pressure sensor in one embodiment and a microscopic photograph of the pressure sensor;

[0043] Fig.21 In one embodiment, a pressure sensor is measured using a standard method;

[0044] Fig. 22 In one embodiment, a pressure sensor is measured at the HHP;

[0045] Fig.23 In one embodiment, the pressure sensor is continuously measured by monitoring the change of HHP;

[0046] Fig.24 Graph showing five measurement results of a micro sensor in one embodiment. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0048] It should be noted that all directional indications in the embodiments of the present application (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0049] In addition, the descriptions of "first", "second", etc. in this application are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "multiple groups" means at least two groups, such as two groups, three groups, etc., unless otherwise clearly and specifically defined.

[0050] In this application, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, a physical connection, or a wireless communication connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0051] In addition, the technical solutions between the various embodiments of the present application can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0052] The present application provides a wireless sensing method based on Hamiltonian jump point following, such as Figure 1 The flowchart shown, in one embodiment, includes:

[0053] Step 102, establish an oscillation model of the wireless sensor; the oscillation model includes: a first oscillator, a second oscillator, an amplifier and a loss device, the first oscillator and the second oscillator are arranged at an interval, the amplifier is connected in parallel with the first oscillator, and the loss device is connected in parallel with the second oscillator.

[0054] Specifically:

[0055] The first oscillator is a parallel resonant circuit, comprising: a first inductor L1 and a first capacitor C1; the first inductor, the first capacitor and the gain device are connected in parallel;

[0056] The second oscillator is a parallel resonant circuit, comprising: a second inductor L2 and a second capacitor C2; the second inductor, the second capacitor and the loss device are connected in parallel;

[0057] The gainer is a negative impedance converter (NIC)-R1 to provide gain, and the negative impedance converter includes a saturable amplifier to introduce nonlinearity;

[0058] The loss device is the programmable resistor R2.

[0059] The first oscillator and the amplifier form a gain end, and the gain end is used as a reader. The second oscillator and the loss device form a loss end, and the loss end is used as a sensor.

[0060] in:

[0061] The first capacitor and the second capacitor are both programmable capacitors, and the first inductor and the second inductor are tuned to corresponding resonant frequencies using the first capacitor and the second capacitor respectively:

[0062]

[0063] In the formula, ω n is the resonant frequency, L n is the inductance, C n is a programmable capacitor, n=1,2.

[0064] In this step, if Figure 2 and Figure 3 As shown, an oscillation model of a wireless sensor is established, two spiral coils (i.e., a first inductor L1 and a second inductor L2) are manufactured on a printed circuit board, the resonant frequency of the first inductor L1 is adjusted by a capacitor C1, and the resonant frequency of the second inductor L2 is adjusted by a capacitor C2 and a programmable resistor R2, and CC is controlled by a computer.

[0065] Step 104, after the first oscillator and the second oscillator are coupled through mutual inductance, the resistance values ​​of the second oscillator and the loss device are changed, and a Riemann surface is formed by clockwise and counterclockwise rotation.

[0066] Among them, mutual inductance produces wireless coupling resonance, and the internal coupling strength is determined by the spacing between the inductors:

[0067]

[0068] Where κ is the coupling strength, ω1 is the resonant frequency of the first inductor, ω2 is the resonant frequency of the second inductor, and M s is the mutual inductance between the reader and the sensor, ω is the resonant frequency after coupling, L1 is the first inductance, and L2 is the second inductance.

[0069] Changing the resistance of the second oscillator and the loss device includes: changing the resistance of the second capacitor (ie, the programmable capacitor C2) of the second oscillator and the loss device (ie, the programmable resistor R2).

[0070] In this step, if Figure 4 The counterclockwise rotating Riemann surface shown is Figure 5 The clockwise rotating Riemann surface shown is observed at R2 <R EP and R2>R EP Resonant frequencies in both regions.

[0071] Step 106: When the Riemann surface generates a Hamiltonian jumping point HHP, a pair of singular points EPs is obtained.

[0072] In this step, if Figure 6 As shown, in R2 <R EP =1.141, where the curve represents the frequency response of the standard method, the broken line represents the Hamiltonian jump when sweeping C2 in the counterclockwise and clockwise directions, and the two hollow circles represent a pair of HHP singular points.

[0073] like Figure 7 As shown, the sensing at the HHP is compared with the standard method. By continuously scanning C2 forward and backward and recording the location where the Hamiltonian jump occurs, a pair of HHPs is confirmed and the linear relationship between the capacitance perturbation and the HHP displacement is verified. When the sensor is subjected to a 3.8pF step perturbation, both HHPs move to the right, showing a linear relationship with the perturbation, as shown in Figure 1. Figure 8 shown.

[0074] Step 108, performing wireless sensing based on the singularity point to improve the responsiveness of the wireless sensor.

[0075] In this step, a Hamiltonian jump occurs during the EP wrap, and the system undergoes a state flip during the adiabatic evolution, but returns to the initial state through a non-adiabatic transition, so a Hamiltonian jump occurs in the opposite direction. Benefiting from the large frequency response at the Hamiltonian jumping point (Hamilton Hopping Pint, HHP), a 60-fold responsiveness enhancement is achieved compared to the standard sensing method without using HHP.

[0076] This embodiment implements a high-response wireless sensor by tracking Hamiltonian jump points in an EP system. The specific principle is analyzed as follows.

[0077] like Fig. 9 As shown, an EP system is constructed using coupled mode theory. The EP system consists of two oscillators (i.e., a pair of LC resonators, one of which has a loss component as a sensor, and the other resonator has a saturated gain and acts as a reader. The reader, i.e., the gain resonator, is a self-oscillating instrument, and the steady-state frequency is read out by an oscilloscope). Its resonant frequency is ω 1，2 , that is, the resonant frequency of the reader and sensor is ω 1，2 , the coupling ratio (coupling strength) is κ, the loss rate is γ, and the saturation gain is g sat , the disturbance of the sensor is Δω. The Hamiltonian of the system is expressed as:

[0078]

[0079] Considering the time harmonic signal e -iωt The above equation is equivalent to the circuit theory. According to the derivation of the reference, the characteristic frequency can be calculated from the characteristic equation:

[0080] [i(ω1-ω)+g][i(ω2-ω)-γ]-κ 2 =0 (2)

[0081] The complex-valued eigenfrequency is given by:

[0082]

[0083] Introducing a nonlinear term (i.e., saturation gain g sat ) into the Hamiltonian, the nonlinearity leads to bistability, which leads to the emergence of a pair of HHPs, such as Fig.10 As shown, mode 1 and mode 2 represent two branches in the strong coupling, and the EP marks the location where the two branches split. A pair of HHPs is located on the edge of bistability induced by nonlinearity. In particular, the induced nonlinearity forces the system to oscillate steadily with a purely real eigenfrequency and saturate the gain, while other states with complex-valued eigenfrequencies are prevented from reaching steady-state resonance.

[0084] Taking the characteristic frequency as a real number and separating the real and imaginary parts of equation (3), we obtain:

[0085]

[0086] Then the characteristic equation becomes:

[0087] (ω-ω1)(ω-ω2) 2 +γ 2 (ω-ω1)-k 2 (ω-ω2)=0 (5)

[0088] By numerically solving equation (5) in the parameter space of ω1 and κ, we obtain a self-intersecting solution surface, namely the Riemann surface, assuming that other parameters remain unchanged.

[0089] like Fig.10 As shown, in the Riemann surface, there are three possible solutions. The states with purely real eigenfrequencies (blue and orange) are stable states supported by the system, while the other states (yellow) are always unstable. The EP is located at ω1=ω2 and γ=κ, where the real and imaginary parts of the eigenfrequencies overlap simultaneously.

[0090] like Fig.11 As shown, during the EP wrapping period, a pair of HHPs will appear in the counterclockwise (CCW) and clockwise (CW) directions.

[0091] like Fig.12 As shown, a cross section of the Riemann surface is plotted when κ = 0.05, which can clearly describe the Hamiltonian jump. The dynamics of the system around the EP in both directions are represented by black arrows. Initially, the system is on the upper branch (blue curve) and evolves adiabatically in the counterclockwise direction. Although the other state (red curve) has a zero imaginary eigenfrequency, the system does not switch to that state because the current state is already stable, preventing other states from reaching a steady state. When the system reaches the right HHP, the system undergoes a non-adiabatic transition and spontaneously switches to another steady state for resonance. The system dynamics in the clockwise direction are similar, but the left HHP is displaced from the right HHP, resulting in a unique bistability and the emergence of a pair of HHPs.

[0092] Consider ω2 as a sensor, the sensing mechanism is as follows Fig.13 As shown in Figure 2, when the sensor is disturbed by Δω, HHP moves with Δω as a linear function. The linear relationship between HHP and Δω is shown in Figure 2. Fig.14 shown.

[0093] Therefore, the sensor's perturbations can be recorded by changes in the HHP, sensing in a highly responsive manner, thanks to the Hamiltonian jump. The responsiveness at the HHP is the characteristic frequency difference of the state between the upper and lower branches, which can be approximated as:

[0094]

[0095] In contrast, the standard method uses a single inductive coil to read an LC sensor, whose responsivity is:

[0096]

[0097] When the perturbation is very weak, the HHP-based sensing method can produce a larger response than the standard method, and higher coupling strength or lower loss can further increase the responsiveness of HHP sensing.

[0098] In another embodiment, the method further includes: reducing the distance between the first oscillator and the second oscillator, increasing the resistance of the loss device and / or reducing the change rate of the resistance in the second oscillator to reduce the noise of the HHP.

[0099] in:

[0100] Reducing the distance between the first oscillator and the second oscillator can increase the coupling strength between the first oscillator and the second oscillator, thereby reducing the noise of the HHP and further improving the responsiveness.

[0101] Increasing the resistance of the loss device can reduce the loss rate and thus reduce the noise of the HHP.

[0102] Reducing the change rate of the resistance value in the second oscillator can increase the change time without increasing the number of changes (ie, the number of scans), thereby reducing noise and further improving responsiveness.

[0103] At the same time, reducing the distance between the first oscillator and the second oscillator, increasing the resistance of the loss device, and reducing the change rate of the resistance in the second oscillator can further reduce the noise of the HHP.

[0104] In this embodiment, if Fig.15 The relationship between the responsiveness enhancement factor and the frequency change is shown. The shaded area shows the noise of the HHP, which determines its sensing limit.

[0105] The noise of HHP is studied by Monte Carlo simulation method as follows.

[0106] Equation (5) is numerically calculated at different ω1, and the Gaussian distribution of the disturbance is considered, and its probability density function is as follows:

[0107]

[0108] Here, σ represents the standard deviation of the noise intensity.

[0109] like Fig.16As shown, Monte Carlo simulation is performed under the condition of Gaussian distribution of ω2 (standard deviation σ = 0.005) and coupling ratio κ / γ = 1.25 to obtain the numerical results of characteristic frequencies. The characteristic frequencies are distributed in the probability density represented by the color brightness in the figure. The role of noise is to transform a purely real characteristic frequency into a complex value with an imaginary part, which transforms the steady-state resonance into an unsteady-state resonance within a certain spectrum range and leads to distributed HHP.

[0110] like Fig.17 As shown, Monte Carlo simulations were performed under the conditions of Gaussian distribution of ω2 (standard deviation σ = 0.005) and coupling ratio κ / γ = 1.5, and numerical results of the characteristic frequencies were obtained. HHP is distributed with uncertainty, reflecting the noise, and the noise of HHP can be reduced in the stronger coupling region, that is, HHP is reduced in the case of strong coupling. The underlying mechanism is that EP is very sensitive to noise, because the non-orthogonality of the characteristic state at EP is the largest, so operating the system away from EP can reduce the non-orthogonality and increase the robustness to noise.

[0111] like Fig.18 and Fig.19 As shown, the noise can be reduced by time evolution, that is, by slowly changing the parameters of the system. In the case of strong coupling, the noise of HHP is measured by repeatedly scanning the programmable capacitor C2 in the forward and backward directions 5 times, that is, the uncertainty. Under slow adjustment of C2, the noise of HHP can be reduced, where the minimum resolution of the programmable capacitor is 0.376pF, and the shading represents the standard deviation of the 5 trials.

[0112] The wireless sensing method based on Hamiltonian jump point following introduces a nonlinear term, saturation gain, which leads to the emergence of a pair of HHPs. By tracking the Hamiltonian jump caused by non-adiabatic transitions during dynamic surround, high-sensitivity detection of sensor disturbances is achieved. The Hamiltonian jump is nonlinearly split under small disturbances, resulting in the simultaneous degradation of eigenvalues ​​and eigenvectors, thereby generating an amplified characteristic frequency response, enhancing the frequency response of the system, and achieving a significant enhancement of the frequency response at the Hamiltonian jump point. Furthermore, the sensor's responsiveness can be further improved by adjusting the system parameters, especially the coupling strength and loss. In addition, the noise analysis at the HHP was performed using Monte Carlo simulation, and the noise level was reduced by operating the system in a strong coupling region and slowly adjusting the system parameters through time evolution. This application utilizes EPs to improve the responsiveness of the LC sensor to a greater extent and overcome the noise of the sensor at the singular point. The principle is clear and the application range is wide. It provides new ideas for the development of sensor technology and can play an important role in the industrial and biomedical fields, such as: optical microcavity sensors, biophotonic sensors, microscale pressure sensors and other equipment, especially suitable for biomedical implants and other fields, such as: measuring intraocular pressure, intracranial pressure, pulmonary artery pressure and other biomedical implants. In addition, it also has great potential in the fields of photonics and electronics and can be used for sensitive biomolecule detection and physiological status monitoring.

[0113] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0114] In a specific embodiment, taking a microscale pressure sensor as an example, such a sensor is expected to become a biomedical implant, the microscale pressure sensor is tested, and the specific steps are as follows:

[0115] Step 1, connect this capacitive sensor to the circuit of the present application as a variable capacitor that can be adjusted by gas pressure through a syringe.

[0116] Step 2, test the characteristics of the pressure sensor by increasing the air pressure from 1 bar to 3 bar. Fig. 20As shown in Figure 1, the capacitance of the sensor increases linearly with the increase of air pressure, from 0pF to 3pF. Since the resistance of the sensor is greater than 1×10 6 Ω, in the parallel sensor circuit, the loss caused by the sensor can be ignored.

[0117] Step 3, evaluate the sensing performance of the method without HHP. Fig.21 As shown, under weak capacitive perturbations (<3pF), weak responsiveness and high noise (shaded area) can be observed.

[0118] Step 4, measure the sensor at one HHP. Fig. 22 The frequency response of the sensor capacitance change is shown, and a clear frequency jump can be observed, indicating that the sensing limit drops to the sub-picofarad level (<1pF). Although there is some inevitable noise in the system under strong coupling, the frequency response is much higher than the noise as long as the sensor parameters pass through the HHP. Compared with the standard method without HHP, the responsiveness of the microscale sensor is improved by 40 times at the HHP, that is, a 40-fold responsiveness enhancement is achieved in the detection of the microscale pressure sensor, and the detection limit is reduced to sub-picofarad.

[0119] Step 5, monitor the change of HHP to demonstrate the multi-state measurement of the microscale sensor. During the measurement, the programmable capacitor C2 is scanned slowly and repeatedly to monitor the change of HHP. The resolution of C2 is 0.376pF, and no additional noise is introduced to HHP under the slow surround of EP. When the sensor capacitance changes linearly, HHP also changes linearly.

[0120] Step 6, in the previous step, only the forward demodulation is applied, because in the backward tuning, the change of HHP is the same as the linear change of the forward demodulation. Fig.23 As shown, changes in sensor capacitance in the 3pF range can be consistently read out via changes in HHP.

[0121] Step 7, such as Fig.24 As shown, the above measurement was repeated 5 times. The results show that the uncertainty of HHP (i.e., HHP noise) in the measurement of the microscale sensor is about 0.15pF, indicating that the sensing limit, i.e., the detection limit, of the current setting reaches sub-picofarad (<1 pF). The noise can be further reduced by using digital components with higher tuning accuracy and less electronic noise.

[0122] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A wireless sensing method based on Hamiltonian jumping point following, characterized in that: include: Establish oscillation model of wireless sensor; The oscillation model comprises: a first oscillator, a second oscillator, an amplifier and a loss device, the first oscillator and the second oscillator are arranged at intervals, the amplifier is connected in parallel with the first oscillator, and the loss device is connected in parallel with the second oscillator; When the first oscillator and the second oscillator are coupled through mutual inductance, the resistance values ​​of the second oscillator and the loss device are changed, and a Riemann surface is formed by clockwise and counterclockwise rotation; When a Hamiltonian jump point is generated on the Riemann surface, a pair of singular points is obtained; Performing wireless sensing according to the singular point to improve the responsiveness of the wireless sensor; The Hamiltonian is expressed as: In the formula, κ is the coupling rate, γ is the loss rate, is the resonant frequency of the first inductor, is the resonant frequency of the second inductor; Calculate the eigenfrequency from the characteristic equation: The complex-valued eigenfrequency is given by: Introducing Saturation Gain Into the Hamiltonian, nonlinearity leads to bistability, which leads to the emergence of a pair of HHPs. Taking the characteristic frequency as a real number, separating the real and imaginary parts of equation (3) yields: Then the characteristic equation becomes: By numerically solving equation (5) in the parameter space of ω1 and κ, the Riemann surface is obtained.

2. The wireless sensing method based on Hamiltonian jumping point following according to claim 1 is characterized in that: Also includes: The distance between the first oscillator and the second oscillator is reduced to improve the coupling strength between the first oscillator and the second oscillator and reduce noise.

3. The wireless sensing method based on Hamiltonian jumping point following according to claim 2 is characterized in that: Also includes: Increasing the resistance of the loss device will reduce the loss rate and reduce noise.

4. The wireless sensing method based on Hamiltonian jumping point following according to claim 3 is characterized in that: Also includes: The rate at which the resistance in the second oscillator changes is reduced to reduce noise.

5. The wireless sensing method based on Hamiltonian jumping point following according to any one of claims 1 to 4, characterized in that: The first oscillator is a parallel resonant circuit, comprising: a first inductor and a first capacitor; The first inductor, the first capacitor, and the booster are connected in parallel.

6. The wireless sensing method based on Hamiltonian jumping point following according to claim 5 is characterized in that: The second oscillator is a parallel resonant circuit, comprising: a second inductor and a second capacitor; The second inductor, the second capacitor, and the loss device are connected in parallel.

7. The wireless sensing method based on Hamiltonian jumping point following according to claim 6 is characterized in that: Changing the resistance of the second oscillator and the loss device includes: changing the resistance of the second capacitor of the second oscillator and the loss device.

8. The wireless sensing method based on Hamiltonian jumping point following according to claim 7, characterized in that: The first capacitor and the second capacitor are both programmable capacitors, and the first inductor and the second inductor are tuned to corresponding resonant frequencies using the first capacitor and the second capacitor, respectively.

9. The wireless sensing method based on Hamiltonian jumping point following according to any one of claims 1 to 4, characterized in that: The gain device is a negative impedance converter.

10. The wireless sensing method based on Hamiltonian jumping point following according to any one of claims 1 to 4, characterized in that: The dissipator is a programmable resistor.

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