Control circuit design method for improving temperature stability of chip

Abstract

The invention relates to a control circuit design method for improving temperature stability of a chip. The method comprises the steps that a temperature measuring resistor and a heating resistor are arranged on the surface of a chip and used for measuring the temperature and heating the chip to the rated highest working temperature; preliminarily constructing a chip temperature control circuit, wherein the chip temperature control circuit comprises a plurality of resistors and capacitors of which parameters are to be determined; constructing a chip temperature control circuit simulation model, and performing iterative optimization on the to-be-determined parameters of the resistor and the capacitor and the control parameters of the control circuit based on a preset fitness function, the rated maximum working temperature of the chip and a temperature change process transfer function corresponding to the chip to obtain an optimal parameter value corresponding to a minimum fitness value; and obtaining a control circuit based on the optimal parameter value. According to the method, the chip temperature stability control effect is improved based on circuit-level parameter optimization, an existing chip does not need to be redesigned, the universality of the method is improved, and the chip temperature control cost is reduced.

Description

Technical Field

[0001] The invention belongs to the field of semiconductor technology, and in particular relates to a control circuit design method for improving chip temperature stability. Background Art

[0002] Chips are an important part of the vehicle electronic system and affect the overall performance of the vehicle electronic system. With the increasing number of vehicle sensors, the demand for bandwidth is increasing, and the requirements for clock synchronization in multi-source sensor networks are also increasing. According to the continuous improvement of functional requirements such as multi-sensor data fusion and synchronous display of multiple videos, various end nodes such as cameras and radars in the network need to align timestamps (clock synchronization) to better achieve data fusion, image stitching, etc. Therefore, highly stable chips are needed in the network as signal references in the network. Taking the vehicle inertial measurement unit (IMU) or clock chip as an example, its core precision movable parts will be affected by changes in ambient temperature, resulting in thermal expansion effects. The resulting slight changes in size or stiffness will lead to serious operating frequency drifts of thousands of ppm. With the rapid development of the smart car industry, intelligent autonomous driving and multi-sensor fusion have higher and higher performance requirements for IMU, clock and other chips. The operating frequency drift of these vehicle chips caused by ambient temperature changes will cause inaccurate navigation or sensor clock synchronization errors, which will affect the normal operation and safety of the vehicle system. Therefore, it is very important to improve the temperature stability of these vehicle chips. Although some passive compensation methods such as composite materials have been studied, it is difficult to achieve the highest stability. The existing methods only improve the material properties and cannot completely overcome temperature changes. Studies have shown that using a temperature control solution to keep the chip temperature stable is one of the most effective ways to solve the frequency drift of clock chips. However, the effective methods currently disclosed are almost all optimized from the perspective of chip design. It is necessary to redesign the chip structure or even change the process to meet the temperature control requirements, which requires huge economic and design costs. Therefore, it is of great practical significance to design a low-cost and feasible solution to improve the temperature stability of the chip. Summary of the invention

[0003] In view of the above analysis, the present invention aims to disclose a control circuit design method for improving chip temperature stability. The chip temperature stability control effect is improved based on circuit-level parameter optimization, and there is no need to redesign the existing chip, which improves the versatility of the method and reduces the chip temperature control cost.

[0004] The purpose of the present invention is mainly achieved through the following technical solutions:

[0005] The present invention discloses a control circuit design method for improving chip temperature stability, comprising:

[0006] A temperature measuring resistor RT and a heating resistor RH are arranged on the surface of the chip; the temperature measuring resistor RT is used to detect the temperature of the chip, and the heating resistor RH is used to heat the chip to a rated maximum operating temperature;

[0007] A chip temperature control circuit is preliminarily constructed, the chip temperature control circuit comprising a sensing unit for detecting the temperature change of the temperature measuring resistor RT, a PID control unit for generating a control signal, and an execution unit for driving and heating the heating resistor RH based on the control signal; the chip temperature control circuit comprises a plurality of resistors and capacitors with parameters to be determined;

[0008] Constructing a chip temperature control circuit simulation model, iteratively optimizing the parameters of the resistor and capacitor to be determined and the control parameters of the control circuit based on a preset fitness function, the rated maximum operating temperature of the chip and the temperature change process transfer function corresponding to the chip, and obtaining the optimal parameter value corresponding to the minimized fitness value;

[0009] The control circuit is obtained based on the optimal parameter value.

[0010] Furthermore, the sensing unit includes a temperature measuring bridge and a signal amplification module;

[0011] The temperature measurement bridge includes resistors R2, R3 and R4; the signal amplification module includes a differential amplifier and a proportional resistor RG;

[0012] One end of the resistor R4 is electrically connected to the temperature measuring resistor RT, and the other end is electrically connected to the negative input terminal of the operational amplifier of the differential amplifier and is grounded after passing through the resistor R3;

[0013] One end of the temperature measuring resistor RT and the resistor R4 are electrically connected to the input end of the temperature measuring bridge, and the other end is electrically connected to the positive input end of the operational amplifier of the differential amplifier and is grounded after passing through the resistor R2;

[0014] The two gain setting ports of the differential amplifier are electrically connected via a proportional resistor RG, and the output VO of the sensing unit is obtained through the output terminal.

[0015] Further, the PID control unit includes a filter and a controller;

[0016] The filter includes a resistor RTF and a capacitor CTF; the controller includes an operational amplifier and resistors RP1, RP2, RP3, capacitors CP1 and CP2;

[0017] The output VO of the sensing unit passes through the resistor RTF and serves as the input VcIn of the operational amplifier, and is electrically connected to the negative input terminal of the operational amplifier after passing through the resistor RP1 and the capacitor CP1 connected in parallel; the input VcIn of the operational amplifier is grounded after passing through the capacitor CTF, the positive input terminal of the operational amplifier is grounded after passing through the resistor RP3, and the negative input terminal of the operational amplifier is electrically connected to the output terminal of the operational amplifier after passing through the resistor RP2 and the capacitor CP2, so as to obtain the output VcOut of the PID control unit.

[0018] Furthermore, the execution module includes a MOS tube, a resistor RE1, a resistor RE2 and a resistor RE3;

[0019] The output VcOut of the PID control unit is electrically connected to the gate of the MOS tube after passing through the resistor RE1, and the gate is grounded after passing through the resistor RE2; after the resistor RE3, the heating resistor RH and the diode are connected in parallel, one end is electrically connected to the drain of the MOS tube, and the other end is electrically connected to the power supply, and the source of the MOS tube is grounded.

[0020] Furthermore, the output VcOut of the PID control unit is expressed as:

[0021]

[0022] Among them, R p1 , R p2 is the resistance value of resistor RP1 and resistor RP2, C p1 , C p2 is the capacitance value of capacitor CP1 and capacitor CP2.

[0023] Further, based on the multi-strategy fusion PSO algorithm, the resistance, capacitance and control parameters to be determined are iteratively optimized, specifically including:

[0024] S1: randomly initialize the flight position and flight speed of each particle in a particle swarm, and randomly divide the particle swarm into two sub-populations; each particle in the particle swarm includes the parameters to be determined and the control parameter information of the resistance and capacitance;

[0025] S2: performing chip temperature control circuit simulation based on the flight position and flight speed of each particle, and obtaining the fitness value of the position of each particle in the two sub-populations based on a preset fitness function, the rated maximum operating temperature of the chip and the temperature change process transfer function corresponding to the chip;

[0026] S3: obtaining the local extreme point pbest and the global extreme point gbest of each particle based on the minimized fitness value; and based on the local extreme point pbest and the global extreme point gbest, using different position update strategies to update the position and velocity of each particle in the two sub-populations;

[0027] S4: Randomly select P particles from each sub-population and add them to the hybridization pool for hybridization to generate new particles. The P new particles generated by hybridization are added to the two sub-populations to replace the P particles with the lowest fitness in each population.

[0028] S5: Repeat S2-S4, iteratively optimize the position and speed of each particle until the maximum number of iterations is reached, and obtain the optimal parameter value of each particle corresponding to the minimized fitness function; during the iterative optimization process, if the algorithm reaches the maximum continuous non-improvement algebraic value, the particle swarm is perturbed based on the preset perturbation strategy and S2-S4 is repeated for iterative optimization.

[0029] Furthermore, the particles in the particle group include resistor R2, resistor R3, resistor R4, proportional resistor RG, resistor RTF, capacitor CTF, resistor RP1, resistor RP2's resistance values ​​to be determined, capacitor CP1, capacitor CP2's capacitance values ​​to be determined, and PID control parameters proportion P, integral I and differential D.

[0030] Furthermore, the adopting of different position updating strategies to update the position and velocity of each particle in the two sub-populations includes:

[0031] Adopt the adaptive position update strategy to update the position of the first sub-population, which can be expressed as:

[0032]

[0033]

[0034]

[0035] in, and Respectively represent the velocity and position of the i-th particle in the first sub-swarm at the current moment, k represents the number of iterations of the current particle, c1 and c2 are learning factors, ranging from [0,4], r1 and r2 are random numbers between [0,1], ω(k) is the adaptive inertia weight, representing the influence of the velocity value of the previous iteration on the velocity value of the current iteration; is the local extreme point of particle i, is the global extreme point of the current particle swarm;

[0036] The discrete position update algorithm is used to update the position of the second sub-population, which can be expressed as:

[0037]

[0038]

[0039] in, and Respectively represent the velocity and position of the jth particle in the second population at the current moment, r3 and r4 are random numbers between [0,2] and [1,2], ω is a fixed inertia weight, G k is the average position value of multiple random particles.

[0040] Furthermore, the particles added to the hybridization pool are hybridized by the following formula to generate new particles:

[0041]

[0042] The new particle speed is:

[0043]

[0044] in, is the position of the new particle generated after hybridization, is the speed of the new particles generated after hybridization, and They represent the velocity and position of the mth particle in the first subpopulation at the current moment, respectively. and They represent the velocity and position of the mth particle in the second sub-population at the current moment, respectively, and α is a random number between [0,1].

[0045] Furthermore, the disturbance strategy includes:

[0046] Generate n disturbance points based on the current global optimal position, expressed as:

[0047]

[0048] Where n is the search dimension; R i An equally probable random number in the interval [0,1] is generated independently for each particle; is the global optimal extreme point of the kth iteration;

[0049] Determine the distance between each particle and the disturbance point If the distance of a particle exceeds the threshold, the particle is expelled at the preset expulsion distance to keep it away from the disturbance point;

[0050] The preset expulsion distance is expressed as:

[0051]

[0052] Among them, k is the current number of iterations, τ is the maximum number of iterations;

[0053] The particle position after being driven away is updated as follows:

[0054]

[0055] in, To update the position of the particle after being driven away, c and d are random numbers between [0,1] and [1,2] respectively; τ is the maximum number of iterations.

[0056] The present invention can achieve at least the following beneficial effects:

[0057] The present invention arranges temperature measuring resistors and heating resistors on the surface of the chip, controls the temperature of the chip through a sensing circuit, a control circuit and an execution circuit, and integrates a PSO algorithm through a multi-strategy, improves the standard PSO through multi-population cross-hybridization, an adaptive operator and a disturbance point strategy, optimizes and selects multiple resistors and capacitors and PID control parameters in the sensing circuit, the control circuit and the execution circuit, avoids the problem of time-consuming and labor-intensive manual debugging and poor effect, improves the temperature control effect without changing the chip structure, and reduces the control cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0059] Figure 1 A flow chart of a control circuit design method for improving chip temperature stability in an embodiment of the present invention;

[0060] Figure 2 A schematic diagram of the positions of the temperature measuring resistor and the heating resistor in an embodiment of the present invention;

[0061] Figure 3 is a schematic diagram of a sensing unit circuit in an embodiment of the present invention;

[0062] Figure 4 Schematic diagram of the PID control unit circuit in an embodiment of the present invention;

[0063] Figure 5 is a schematic diagram of an execution unit circuit in an embodiment of the present invention;

[0064] Figure 6 is the dynamic temperature characteristic of the clock chip in the embodiment of the present invention;

[0065] Figure 7A schematic diagram of a multi-strategy fusion PSO algorithm parameter optimization process in an embodiment of the present invention;

[0066] Figure 8 This is a schematic diagram of control performance of a control circuit starting from room temperature in an embodiment of the present invention;

[0067] Fig. 9 Schematic diagram of control performance of the control circuit when the temperature suddenly changes in an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used to illustrate the principles of the present invention together with the embodiments of the present invention.

[0069] The embodiment of the present invention discloses a control circuit design method for improving chip temperature stability. Figure 1 As shown, the following steps are included:

[0070] Step S1: a temperature measuring resistor RT and a heating resistor RH are arranged on the surface of the chip; the temperature measuring resistor RT is used to detect the temperature of the chip, and the heating resistor RH is used to heat the chip to a rated maximum operating temperature;

[0071] Specifically, this embodiment takes the vehicle-mounted clock chip as an example. In order to keep the clock chip at a constant temperature at all times, in the absence of effective heat dissipation and cooling methods, the temperature of the clock chip needs to be maintained at a high level at all times to avoid chip temperature fluctuations. For automotive-grade chips, their operating temperature range needs to meet -40°C to 125°C. Therefore, for automotive-grade chips, the operating temperature of the clock chip can be maintained in an environment with a rated maximum operating temperature of 125°C at all times to avoid the problem of unstable chip temperature due to the inability to effectively cool down due to the increase in ambient temperature. In order to achieve the above functions, this embodiment sets a temperature measuring resistor RT and a heating resistor RH on the surface of the chip. The temperature measuring resistor RT is used to measure the temperature of the chip and provide an input signal for the chip temperature control circuit. The heating resistor RH is driven by the output signal of the chip temperature control circuit to provide a constant temperature for the chip, thereby improving its temperature stability. Figure 2 As shown, in this embodiment, the clock chip is packaged as a top module on the bottom module, and the bottom module is used to provide support. The temperature measuring resistor RT and the heating resistor RH are attached to the upper surface of the bottom module in the form of a metal film, contacting the core part of the clock chip. The sensing function of the temperature measuring resistor RT is realized by virtue of the sensitivity of the metal to temperature, and the heating resistor RH is heated by applying a DC voltage to the heating resistor RH, and the clock chip is heated by the electrothermal effect. Since the physical scale inside the clock chip is in the micron order and has a good heat-insulating shell, the temperature inside the chip can be approximately regarded as uniform.

[0072] Step S2: Preliminarily constructing a chip temperature control circuit, the chip temperature control circuit comprising a sensing unit for detecting the temperature change of the temperature measuring resistor RT, a PID control unit for generating a control signal, and an execution unit for driving and heating the heating resistor RH based on the control signal; the chip temperature control circuit comprises a plurality of resistors and capacitors with parameters to be determined;

[0073] Specifically, Figure 3 As shown, the sensing unit includes a temperature measuring bridge and a signal amplification module;

[0074] The temperature measurement bridge includes resistors R2, R3 and R4; the signal amplification module includes a differential amplifier and a proportional resistor RG;

[0075] One end of the resistor R4 is electrically connected to the temperature measuring resistor RT, and the other end is electrically connected to the negative input terminal of the operational amplifier of the differential amplifier and is grounded after passing through the resistor R3;

[0076] One end of the temperature measuring resistor RT and the resistor R4 are electrically connected to the input end of the temperature measuring bridge, and the other end is electrically connected to the positive input end of the operational amplifier of the differential amplifier and is grounded after passing through the resistor R2;

[0077] The two gain setting ports of the differential amplifier are electrically connected via a proportional resistor RG, and the output VO of the sensing unit is obtained through the output terminal.

[0078] It should be noted that the common implementation method of the temperature control system is to read the voltage change across the temperature measuring resistor RT, and then transmit the signal to the controller. After being driven by the controller, the heater is started to achieve temperature control. Since the resistance temperature detection (RTD) method is adopted, it is necessary to convert the resistance change caused by temperature into a voltage change to provide an input signal for the controller. The Wheatstone bridge is a precision circuit that converts the temperature measuring resistor RT into a voltage. Three low temperature coefficient resistors (resistors R2, R3 and R4) and the precision temperature measuring resistor RT together form a Wheatstone bridge configuration, and the conversion relationship is as follows:

[0079]

[0080] Temperature bridge V in is the input voltage of the bridge, V out is the output voltage of the bridge, R t is the resistance value of the temperature measuring resistor RT, R2, R3, R4 are the resistance values ​​of the resistors R2, R3 and R4. In practical applications, R2=R3=R4 can be set, and a fixed resistance value R0 can be set, then:

[0081] R t =R0+kt*ΔT;

[0082] Among them, kt is the temperature-resistance change rate of the temperature measuring resistor RT, ΔT is the temperature change. When the temperature changes, R t The change causes the output Vout of the bridge, thereby realizing the conversion of the temperature signal into an electrical signal.

[0083] Furthermore, the error amplification part is mainly implemented by a high-precision low-noise operational amplifier chip INA128, such as Figure 3 As shown, Vcc is the supply voltage, GND is the power ground, and its output can be expressed as:

[0084]

[0085] Among them, ports 2 and 3 of the INA128 chip, Vin- and Vin+, are respectively the positive and negative input stages, and RG is a proportional resistor that determines the multiple of signal amplification. The output signal after the bridge is unbalanced is used as the input signal of INA128 for signal amplification, and the RG resistance value needs to be determined according to the specific system needs.

[0086] Further, the PID control unit includes a filter and a controller;

[0087] The filter includes a resistor RTF and a capacitor CTF; the controller includes an operational amplifier and resistors RP1, RP2, RP3, capacitors CP1 and CP2;

[0088] The output VO of the sensing unit passes through the resistor RTF and serves as the input VcIn of the operational amplifier, and is electrically connected to the negative input terminal of the operational amplifier after passing through the resistor RP1 and the capacitor CP1 connected in parallel; the input VcIn of the operational amplifier is grounded after passing through the capacitor CTF, the positive input terminal of the operational amplifier is grounded after passing through the resistor RP3, and the negative input terminal of the operational amplifier is electrically connected to the output terminal of the operational amplifier after passing through the resistor RP2 and the capacitor CP2, so as to obtain the output VcOut of the PID control unit, as shown in FIG. Figure 4 shown.

[0089] That is, the resistor RTF and the capacitor CTF form a low-pass filter to filter the signal and remove high-frequency noise. Its transfer function is expressed as:

[0090]

[0091] Among them, the input signal is VO, and the output signal is V cIn , R TF is the resistance value of resistor RTF, C TF is the capacitance value of the capacitor CTF, which together determine the cutoff frequency of the filter, and s is the complex frequency domain operator of the transfer function. TF and CTF It needs to be determined according to the specific system needs. The output signal VcIn of the filter is used as the input of the controller. In the design of control systems, PID controllers are widely used due to their simple implementation, good control effect and strong robustness. The implementation form of PID analog circuits is as follows Figure 4 As shown, the op amp chip OPA197 is used, and its output is expressed as:

[0092]

[0093] Among them, R p1 , R p2 is the resistance value of resistor RP1 and resistor RP2, C p1 , C p2 is the capacitance value of capacitor CP1 and capacitor CP2.

[0094] Further, such as Figure 5 As shown, the execution module includes a MOS tube, a resistor RE1, a resistor RE2 and a resistor RE3;

[0095] The output VcOut of the PID control unit is electrically connected to the gate of the MOS tube after passing through the resistor RE1, and the gate is grounded after passing through the resistor RE2; after the resistor RE3, the heating resistor RH and the diode are connected in parallel, one end is electrically connected to the drain of the MOS tube, and the other end is electrically connected to the power supply, and the source of the MOS tube is grounded.

[0096] The output of the control unit is used as the input of the execution module. The output of this part is the thermal power of the heating resistor RH. The chip temperature rise process can usually be regarded as a first-order lag link. Since the physical size of the chip is very small, the lag process can be ignored. This embodiment uses SI4336DY as a driving element. The size of its input voltage determines the length of time the heating resistor RH is powered on for heating, thereby realizing the temperature control function.

[0097] Step S3: constructing a chip temperature control circuit simulation model, iteratively optimizing the parameters of the resistor and capacitor to be determined and the control parameters of the control circuit based on a preset fitness function, the rated maximum operating temperature of the chip and the temperature change process transfer function corresponding to the chip, and obtaining the optimal parameter value corresponding to the minimized fitness value;

[0098] Specifically, before conducting circuit simulation, it is necessary to obtain the temperature change process transfer function corresponding to the chip according to the thermal characteristics of the chip for temperature control simulation. In practical applications, the chip can be placed in a high and low temperature box for testing. Each temperature point is maintained for half an hour to stabilize the test temperature. At the same time, a high-precision digital multimeter is used to measure the resistance value of the temperature measuring electrode, and a linear regression fit is performed on the test data points to obtain the temperature change process transfer function corresponding to the chip. In particular, this embodiment applies a DC voltage to the heating resistor RH to increase the internal temperature of the chip. With the help of the temperature measuring resistor RT, the thermal characteristics of the clock chip can be obtained, such as Figure 6 As shown, the temperature change process transfer function of the clock chip is then obtained.

[0099] Furthermore, the resistance values, capacitance values ​​and PID parameters such as the resistor R2, resistor R3, resistor R4, proportional resistor RG, resistor RTF, capacitor CTF, resistor RP1, resistor RP2, capacitor CP1, capacitor CP2 in the chip temperature control circuit, as well as the PID control parameters such as proportional P, integral I and differential D will affect the temperature control effect. In the actual control system design, it is necessary not only to select PID RP1, RP2, CP1, CP2, but also to optimize the parameters such as R2, R3, R4 in the bridge, the error amplification proportional resistor RG, and RTF and CTF in the filter. Manual debugging is extremely difficult and the performance cannot be guaranteed. However, there are few related studies on optimizing the parameters of the entire control circuit in the prior art, and the control effect of the control circuit needs to be improved. In order to improve the control effect of the chip temperature control circuit, this embodiment adopts a multi-strategy fusion PSO algorithm to adjust the parameters of the control circuit. This algorithm is an improvement on the PSO algorithm. This algorithm inherits the advantages of the PSO algorithm such as fast convergence speed and no influence of initial values ​​when searching for the optimal solution. At the same time, in view of the problem that the particle swarm algorithm is prone to fall into local minima when searching for optimization on complex surfaces in high-dimensional space, combined with the characteristics of the problem, multi-swarm cross-hybridization, adaptive operators and perturbation strategies are adopted to avoid the shortcomings of the traditional PSO algorithm.

[0100] For example, Figure 7 As shown, based on the multi-strategy fusion PSO algorithm, the process of iteratively optimizing the parameters to be determined and the control parameters of the resistor and capacitor includes:

[0101] S31: randomly initializing the flight position and flight speed of each particle in the particle swarm, and randomly dividing the particle swarm into two sub-populations; each particle in the particle swarm includes the parameters to be determined and the control parameter information of the resistance and capacitance;

[0102] More specifically, firstly, based on the resistors R2, R3, R4, proportional resistor RG, resistor RTF, capacitor CTF, resistors RP1, resistor RP2 to be determined resistance values, capacitors CP1, capacitor CP2 to be determined capacitance values, and PID control parameters proportion P, integral I and differential D, a particle swarm is constructed and initialized to obtain a set of random particles {p1, p2, …p n}.

[0103] S32: performing chip temperature control circuit simulation based on the flight position and flight speed of each particle, and obtaining the fitness value of the position of each particle in the two sub-populations based on a preset fitness function, the rated maximum operating temperature of the chip and the temperature change process transfer function corresponding to the chip;

[0104] Specifically, for the PSO optimization method, it is necessary to first determine the objective function (i.e., fitness function). The fitness function used in this embodiment is expressed as:

[0105]

[0106] Among them, g k is the fitness value after the kth iteration, t is the system operation time, and e(t) is the difference between the set chip rated maximum operating temperature and the actual temperature at time t.

[0107] In the control system, steady-state error and adjustment time are the main parameters of system performance. The fitness function of this embodiment can measure the dynamic and steady-state performance of the system. The smaller its value, the better the control circuit performance. A high fitness function value indicates a poor control effect. Therefore, this paper uses this fitness function as the objective function, and iteratively optimizes all parameters in the system through a multi-strategy fusion PSO algorithm to achieve the minimum fitness value and the optimal control effect.

[0108] This embodiment uses a vehicle-mounted clock chip as an example, and its normal operating temperature is -40°C to 125°C, so the rated maximum operating temperature of the chip in this step is set to 125°C, that is, the chip is always kept at an operating temperature of 125°C to avoid the problem of unstable operating temperature due to the inability to effectively cool down after the ambient temperature rises.

[0109] S33: obtaining a local extreme point pbest and a global extreme point gbest of each particle based on the minimized fitness value; and based on the local extreme point pbest and the global extreme point gbest, using different position update strategies to update the position and velocity of each particle in the two sub-populations;

[0110] Specifically, when the PSO algorithm optimizes multiple parameters at the same time, it often converges prematurely and easily falls into local minima because the PSO algorithm needs to take into account both local exploration and global optimization. The present invention improves the standard PSO algorithm by dividing the population, adding multi-population hybridization, adaptive operators and perturbation strategies to improve the parameter optimization effect;

[0111] Preferably, an adaptive position update strategy is used to update the position of the first sub-population, and an adaptive operator is introduced to balance the global search capability in the early stage and the local search capability in the later stage, thereby optimizing the algorithm speed, which is expressed as:

[0112]

[0113]

[0114]

[0115] in, and Respectively represent the velocity and position of the i-th particle in the first sub-swarm at the current moment, k represents the number of iterations of the current particle, c1 and c2 are learning factors, ranging from [0,4], r1 and r2 are random numbers between [0,1], ω(k) is the adaptive inertia weight, representing the influence of the velocity value of the previous iteration on the velocity value of the current iteration; is the local extreme point of particle i, is the global extreme point of the current particle swarm;

[0116] The discrete position update algorithm is used to update the position of the second sub-population, so that the particles tend to move away from the group to increase the probability of jumping out of the local optimum, which is expressed as:

[0117]

[0118]

[0119] in, and Respectively represent the velocity and position of the jth particle in the second population at the current moment, r3 and r4 are random numbers between [0,2] and [1,2], ω is a fixed inertia weight, G k is the average position value of multiple random particles.

[0120] Among them, the position X represents the parameter value, and the speed value v can be regarded as the rate of change of the parameter in each iteration.

[0121] S34: randomly select P particles from each sub-population and add them to the hybridization pool for hybridization to generate new particles, and add the P new particles generated by hybridization to the two sub-populations to replace the P particles with the lowest fitness in each population;

[0122] Specifically, the particles added to the hybridization pool are hybridized by the following formula to generate new particles:

[0123]

[0124] The new particle speed is:

[0125]

[0126] in, is the position of the new particle generated after hybridization, is the speed of the new particles generated after hybridization, and They represent the velocity and position of the mth particle in the first subpopulation at the current moment, respectively. and They represent the velocity and position of the mth particle in the second sub-population at the current moment, respectively, and α is a random number between [0,1];

[0127] That is, this embodiment realizes information exchange between two populations through hybridization and ensures the optimization performance of the algorithm. The existence of the second sub-population can effectively prevent the algorithm from falling into the local optimum. By setting the cross-hybridization operation, the connection between populations is realized, and the poor individuals in the original population are eliminated to enhance the optimization ability of the algorithm.

[0128] S35: Repeat S32-S34, iteratively optimize the position and speed of each particle until the maximum number of iterations is reached, and obtain the optimal parameter value of each particle corresponding to the minimized fitness function; during the iterative optimization process, if the algorithm reaches the maximum continuous non-improvement algebraic value, the particle swarm is perturbed based on the preset perturbation strategy and S2-S4 is repeated for iterative optimization.

[0129] During the iteration process, some particles may have continuous unimproved algebraic values. In this case, we can generate disturbance points based on the preset perturbation strategy to perturb and drive away local particles, and then continue the iterative optimization to avoid falling into the local optimum.

[0130] Specifically, the disturbance strategy includes:

[0131] Generate n disturbance points based on the current global optimal position, expressed as:

[0132]

[0133] Where n is the search dimension; Ri An equally probable random number in the interval [0,1] is generated independently for each particle; is the global optimal extreme point of the kth iteration;

[0134] Determine the distance between each particle and the disturbance point If the distance of a particle exceeds the threshold, the particle will be expelled at the preset expulsion distance to keep it away from the disturbance point and prevent it from falling into the local optimum.

[0135] The distance between the particle and the disturbance point It is expressed as:

[0136]

[0137] The preset expulsion distance is expressed as:

[0138]

[0139] Among them, k is the current number of iterations, τ is the maximum number of iterations;

[0140] The particle position after being driven away is updated as follows:

[0141]

[0142] in, To update the position of the particle after being driven away, c and d are random numbers between [0,1] and [1,2] respectively; τ is the maximum number of iterations.

[0143] The multi-strategy fusion PSO algorithm proposed in this embodiment adds multi-population cross-hybridization, adaptive operator and disturbance point strategy to improve the standard PSO; multi-population cross-hybridization realizes population information exchange, and cooperates with the disturbance point strategy to greatly enhance the ability of the algorithm to jump out of the local optimum and avoid premature maturity of the algorithm in the early stage. At the same time, the elimination of old particles and the adaptive operator strategy ensure the small-scale fine search capability of the algorithm in the later stage.

[0144] It should be noted that this embodiment continuously iterates to find the optimal solution for each parameter through a multi-strategy fusion PSO algorithm. In each iteration, each particle updates the position and speed of the current particle according to the parameters of the local extreme point pbest and the global extreme point gbest in the iteration: the local extreme point pbest and the global extreme point gbest represent the historical local extreme point and global extreme point of the corresponding parameter in the optimization process, respectively. Pbest is an array that stores the historical optimum (local optimal extreme value) of a single particle obtained for each parameter in the iteration process for all parameters to be optimized; similarly, gbest stores the historical optimum (global optimal extreme value) of all particles for all parameters. According to the formula, all particles will search for feasible solutions in the direction of a compromise between their own historical optimum (local optimum) and global optimum.

[0145] Step S4: obtaining the control circuit based on the optimal parameter value;

[0146] After determining the optimal parameter values ​​of each parameter, the embodiment builds a simulation system to conduct experiments. When the control system starts working, due to the large difference between the actual temperature and the target temperature (the rated maximum operating temperature of the chip), the heating power is large, and overshoot occurs within a few minutes. After 200 seconds, the system stabilizes at 125°C. Figure 8 The simulation results show that when the ambient temperature is 25°C, the control system raises the chip temperature to 125°C within 200 seconds, with an overshoot of 5°C (<5%).

[0147] Then, step disturbance and ramp disturbance are applied to the system to simulate the control effect of the system when the external temperature changes suddenly and rapidly, such as Fig. 9 The initial ambient temperature is set to 125°C, and then a step disturbance is added after the system stabilizes (600s) to simulate temperature fluctuations. After the system stabilizes again, a ramp disturbance is applied to simulate rapid temperature changes. Fig. 9 It can be seen that before 500s, the system starts to work and stabilizes the chip temperature at 125℃, which is consistent with Figure 6The process is similar. At 600s, the disturbance of -25℃ ambient temperature change is added to the system, which is drastic enough for the vehicle-mounted chip. From the results, it can be seen that the system can stabilize again within 200s. At 1000s, an ambient temperature change with a slope of 10℃ / min is added to the system until the ambient temperature reaches -40℃. The simulation results show that when facing a rapid ambient temperature change with a change rate of 10℃ / min, the system can still ensure the stability of the chip temperature. The simulation results show that the method proposed in the present invention can effectively ensure that the temperature change of the vehicle-mounted clock chip under conditions such as startup, instantaneous temperature change, and rapid temperature change is not more than 25%, and the stabilization time does not exceed 3 minutes. For the frequency drift temperature characteristics of commonly used clock chips, within the ambient temperature range of -40℃ to 125℃, the maximum frequency drift of the clock chip will reach about -300 to -3000ppm. After temperature control using the method proposed in the present invention, the temperature change of the chip itself will not exceed 25℃ within the ambient temperature range of -40℃ to 125℃. Therefore, the frequency drift of the clock chip, which is most sensitive to temperature, can be controlled within 100ppm. The results show that the method proposed by the present invention can keep the on-board chip stable in the range of -40℃~125℃ ambient temperature, and the temperature stability will be improved by dozens of times compared with ordinary clock chips.

[0148] In summary, the control circuit design method for improving the temperature stability of the chip of the present invention arranges temperature measuring resistors and heating resistors on the surface of the chip, and controls the temperature of the chip through a temperature control circuit including a sensing circuit, a control circuit and an execution circuit, and sets the temperature of the chip to the rated maximum operating temperature, and integrates the PSO algorithm through a multi-strategy, improves the standard PSO through multi-population cross-hybridization, adaptive operators and disturbance point strategies, optimizes and selects multiple resistors and capacitors and PID control parameters in the sensing circuit, the control circuit and the execution circuit, improves the performance of parameter optimization, avoids the time-consuming and labor-intensive manual debugging, and the problem of poor effect, realizes the improvement of temperature control effect without changing the chip structure, improves the versatility of the control method, and reduces the control cost.

[0149] Those skilled in the art will appreciate that all or part of the process of the method in the above embodiment can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0150] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A control circuit design method for improving chip temperature stability, characterized in that: include: A temperature measuring resistor RT and a heating resistor RH are arranged on the surface of the chip; the temperature measuring resistor RT is used to detect the temperature of the chip, and the heating resistor RH is used to heat the chip to a rated maximum operating temperature; A chip temperature control circuit is preliminarily constructed, the chip temperature control circuit comprising a sensing unit for detecting the temperature change of the temperature measuring resistor RT, a PID control unit for generating a control signal, and an execution unit for driving and heating the heating resistor RH based on the control signal; the chip temperature control circuit comprises a plurality of resistors and capacitors with parameters to be determined; Constructing a chip temperature control circuit simulation model, iteratively optimizing the parameters of the resistor and capacitor to be determined and the control parameters of the control circuit based on a preset fitness function, the rated maximum operating temperature of the chip and the temperature change process transfer function corresponding to the chip, and obtaining the optimal parameter value corresponding to the minimized fitness value; The control circuit is obtained based on the optimal parameter value.

2. The control circuit design method for improving chip temperature stability according to claim 1, characterized in that: The sensing unit includes a temperature measuring bridge and a signal amplification module; The temperature measurement bridge includes resistors R2, R3 and R4; the signal amplification module includes a differential amplifier and a proportional resistor RG; One end of the resistor R4 is electrically connected to the temperature measuring resistor RT, and the other end is electrically connected to the negative input terminal of the operational amplifier of the differential amplifier and is grounded after passing through the resistor R3; One end of the temperature measuring resistor RT and the resistor R4 are electrically connected to the input end of the temperature measuring bridge, and the other end is electrically connected to the positive input end of the operational amplifier of the differential amplifier and is grounded after passing through the resistor R2; The two gain setting ports of the differential amplifier are electrically connected via a proportional resistor RG, and the output VO of the sensing unit is obtained through the output terminal.

3. The control circuit design method for improving chip temperature stability according to claim 2, characterized in that: The PID control unit includes a filter and a controller; The filter includes a resistor RTF and a capacitor CTF; the controller includes an operational amplifier and resistors RP1, RP2, RP3, capacitors CP1 and CP2; The output VO of the sensing unit passes through the resistor RTF and serves as the input VcIn of the operational amplifier, and is electrically connected to the negative input terminal of the operational amplifier after passing through the resistor RP1 and the capacitor CP1 connected in parallel; the input VcIn of the operational amplifier is grounded after passing through the capacitor CTF, the positive input terminal of the operational amplifier is grounded after passing through the resistor RP3, and the negative input terminal of the operational amplifier is electrically connected to the output terminal of the operational amplifier after passing through the resistor RP2 and the capacitor CP2, so as to obtain the output VcOut of the PID control unit.

4. The control circuit design method for improving chip temperature stability according to claim 3, characterized in that: The execution module includes a MOS tube, a resistor RE1, a resistor RE2 and a resistor RE3; The output VcOut of the PID control unit is electrically connected to the gate of the MOS tube after passing through the resistor RE1, and the gate is grounded after passing through the resistor RE2; after the resistor RE3, the heating resistor RH and the diode are connected in parallel, one end is electrically connected to the drain of the MOS tube, and the other end is electrically connected to the power supply, and the source of the MOS tube is grounded.

5. The control circuit design method for improving chip temperature stability according to claim 3, characterized in that: The output VcOut of the PID control unit is expressed as: Among them, R p1 , R p2 is the resistance value of resistor RP1 and resistor RP2, C p1 , C p2 is the capacitance value of capacitor CP1 and capacitor CP2.

6. The control circuit design method for improving chip temperature stability according to claim 4, characterized in that: Based on the multi-strategy fusion PSO algorithm, the resistance, capacitance and control parameters to be determined are iteratively optimized, specifically including: S31: randomly initializing the flight position and flight speed of each particle in the particle swarm, and randomly dividing the particle swarm into two sub-populations; each particle in the particle swarm includes the parameters to be determined and the control parameter information of the resistance and capacitance; S32: performing chip temperature control circuit simulation based on the flight position and flight speed of each particle, and obtaining the fitness value of the position of each particle in the two sub-populations based on a preset fitness function, the rated maximum operating temperature of the chip and the temperature change process transfer function corresponding to the chip; S33: obtaining a local extreme point pbest and a global extreme point gbest of each particle based on the minimized fitness value; and based on the local extreme point pbest and the global extreme point gbest, using different position update strategies to update the position and velocity of each particle in the two sub-populations; S34: Randomly select P particles from each sub-population and add them to the hybridization pool for hybridization to generate new particles, and add the P new particles generated by hybridization to the two sub-populations to replace the P particles with the lowest fitness in each population; S35: Repeat S32-S34, iteratively optimize the position and speed of each particle until the maximum number of iterations is reached, and obtain the optimal parameter value of each particle corresponding to the minimized fitness function; during the iterative optimization process, if the algorithm reaches the maximum continuous non-improvement algebraic value, then perturb the particle swarm based on the preset perturbation strategy and repeat S32-S34 for iterative optimization.

7. The control circuit design method for improving chip temperature stability according to claim 6, characterized in that: The particles in the particle group include resistor R2, resistor R3, resistor R4, proportional resistor RG, resistor RTF, capacitor CTF, resistor RP1, resistor RP2's resistance values ​​to be determined, capacitor CP1, capacitor CP2's capacitance values ​​to be determined, and PID control parameters proportion P, integral I and differential D.

8. The control circuit design method for improving chip temperature stability according to claim 6, characterized in that: The adopting different position updating strategies to update the position and velocity of each particle in the two sub-populations comprises: Adopt the adaptive position update strategy to update the position of the first sub-population, which can be expressed as: in, and Respectively represent the velocity and position of the i-th particle in the first sub-swarm at the current moment, k represents the number of iterations of the current particle, c1 and c2 are learning factors, ranging from [0,4], r1 and r2 are random numbers between [0,1], ω(k) is the adaptive inertia weight, representing the influence of the velocity value of the previous iteration on the velocity value of the current iteration; is the local extreme point of particle i, is the global extreme point of the current particle swarm; The discrete position update algorithm is used to update the position of the second sub-population, which can be expressed as: in, and Respectively represent the velocity and position of the jth particle in the second population at the current moment, r3 and r4 are random numbers between [0,2] and [1,2], ω is a fixed inertia weight, G k is the average position value of multiple random particles.

9. The control circuit design method for improving chip temperature stability according to claim 6, characterized in that: The particles added to the hybridization pool are hybridized using the following formula to generate new particles: The new particle speed is: in, is the position of the new particle generated after hybridization, is the speed of the new particles generated after hybridization, and They represent the velocity and position of the mth particle in the first subpopulation at the current moment, respectively. and They represent the velocity and position of the mth particle in the second sub-population at the current moment, respectively, and α is a random number between [0,1].

10. The control circuit design method for improving chip temperature stability according to claim 6, characterized in that: The disturbance strategy includes: Generate n disturbance points based on the current global optimal position, expressed as: Where n is the search dimension; R i An equally probable random number in the interval [0,1] is generated independently for each particle; is the global optimal extreme point of the kth iteration; Determine the distance between each particle and the disturbance point If the distance of a particle exceeds the threshold, the particle is expelled at the preset expulsion distance to keep it away from the disturbance point; The preset expulsion distance is expressed as: Among them, k is the current number of iterations, τ is the maximum number of iterations; The particle position after being driven away is updated as follows: in, To update the position of the particle after being driven away, c and d are random numbers between [0,1] and [1,2] respectively; τ is the maximum number of iterations.

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