Infrared background simulation cooling method suitable for spliced semiconductor

Abstract

The invention relates to the technical field of precise temperature control, and discloses a spliced semiconductor infrared background simulation cooling method, which is applied to a system with a plurality of independent cooling areas, and comprises the following steps: acquiring the working load and the cooling medium inlet temperature of each cooling area; based on the on-line thermal model, the working load and the preset target temperature, reversely solving and calculating the required reference cooling water flow velocity; and the corresponding micro-flow regulating valve is driven to regulate the flow speed, so that feed-forward control is realized. The method further comprises the steps that the actual outlet temperature of the area is obtained; the outlet temperature is calculated and predicted based on forward solution of the online thermal model; calculating a model residual error between the actual outlet temperature and the predicted outlet temperature; and using the model residual error to iteratively update the updatable model parameters in the online thermal model. According to the method, the response speed of temperature control is increased through work load feedforward, drifting of the heat transfer characteristic of the system is compensated in a self-adaptive mode through the model, and long-term high precision of control is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of precision temperature control technology, and in particular to a method for simulating infrared background cooling of spliced ​​semiconductors. Background Technology

[0002] In large-scale infrared scene simulation testing, a large-area infrared background simulator is often constructed by splicing multiple semiconductor modules (such as resistor arrays or infrared emitting devices). To ensure that the simulator can accurately reproduce the infrared radiation characteristics of the target scene, strict temperature control of the semiconductor devices is necessary to remove the waste heat generated during operation. However, with the increase in simulator scale and the improvement in dynamic performance requirements, existing liquid cooling technologies have revealed several limitations.

[0003] Existing cooling solutions for such modular devices typically employ an integrated backplane flow channel design or a globally uniform flow control strategy. This design proves inadequate when dealing with uneven heat loads across sub-modules within the modular array. In actual operation, infrared background images often exhibit complex spatial distributions, leading to significant differences in the heat output of semiconductor devices in different regions. A globally uniform cooling flow rate cannot provide targeted, enhanced heat dissipation to high-heat-load areas, nor can it reduce flow in low-heat-load areas to maintain temperature balance. This spatial mismatch results in poor temperature uniformity across the entire array, making it prone to localized overheating or undercooling, thus affecting the accuracy of infrared radiation simulation.

[0004] Furthermore, traditional temperature control logic often relies on simple temperature feedback mechanisms (such as PID control). The system needs to wait for the temperature sensor to detect a deviation in the temperature of the cooling medium or device before driving the actuator to adjust the flow rate. Due to the physical time delay in heat conduction and fluid transport, this lag-based adjustment method is difficult to adapt to the rapidly changing dynamic scenarios in infrared background simulations. When the workload of semiconductor devices undergoes a step change, the lag in feedback control can lead to significant temperature overshoot or fluctuations, making it difficult to stabilize back at the setpoint in a short time, thus limiting the simulator's ability to reproduce highly dynamic thermal scenarios.

[0005] On the other hand, the physical heat transfer characteristics of a cooling system are not static. With long-term operation, minute amounts of fouling will occur inside the cooling channels, and the thermal interface materials (such as silicone grease) will age or dry out, leading to a gradual decrease in the actual effective heat transfer coefficient. However, existing control algorithms are typically based on fixed parameters or static models calibrated at the factory. These fixed models cannot detect the decline in the system's heat transfer capacity, causing control commands to gradually deviate from actual physical requirements. Over time, this deviation between the model and the actual system introduces steady-state errors that are difficult to eliminate, making it impossible to guarantee the long-term control accuracy of the equipment. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method for simulating infrared background cooling of spliced ​​semiconductors, which addresses the shortcomings of the prior art and solves the problems of poor temperature uniformity, lag in dynamic response, and drift in long-term control accuracy when the prior art is dealing with non-uniform and dynamically changing heat loads.

[0007] To address the aforementioned technical problems, this invention provides a method for simulating infrared background cooling of spliced ​​semiconductors, which is applied to a cooling system with a partitioned cooling backplate.

[0008] In one embodiment, the partitioned cooling backplate includes: A semiconductor backplane, wherein a sealing strip groove is formed on the semiconductor backplane; A silicone sealing strip installed in the groove of the sealing strip; Cooling cover plate; the cooling cover plate is provided with protrusions for extruding silicone sealing strips; Water circulation grooves are provided on both the semiconductor backplate and the cooling cover plate; The micro-flow regulating valve is installed on the pipe connecting the cooling cover plate to the water inlet; The temperature sensor is installed on the pipe connecting the cooling cover to the water outlet. The water circulation groove is physically divided into multiple cooling zones.

[0009] The cooling system also includes micro-flow regulating valves and temperature sensors corresponding to the cooling zones, as well as a controller.

[0010] The micro-flow regulating valve is preferably installed on the pipe connecting the cooling cover plate to the water inlet; The temperature sensor is preferably installed on the pipe connecting the cooling cover plate to the water outlet.

[0011] The method is executed by the controller and specifically includes the following steps: S1. Input Acquisition: The controller acquires at least the workload of the cooling zone and the inlet temperature of the cooling medium in the cooling system. The workload is an electrical signal representing the heat that will be generated in the cooling zone.

[0012] S2. Calculate the reference flow rate: Based on the workload, the inlet temperature of the cooling medium, an online thermal model, and the preset target temperature of the cooling zone, the controller calculates the reference cooling water flow rate required for the cooling zone. The online thermal model contains updatable model parameters.

[0013] In one implementation, this step specifically involves using the updatable model parameters in the online thermal model to obtain the baseline cooling water flow rate by inversely solving the online thermal model.

[0014] S3, Feedforward Execution: The controller controls the micro-flow regulating valve corresponding to the cooling zone to adjust the cooling water flow rate to the reference cooling water flow rate before the heat is fully generated.

[0015] S4. Obtain measured data: The controller obtains the actual outlet temperature of the cooling area through the temperature sensor corresponding to the cooling area.

[0016] S5. Calculate prediction data: Based on the online thermal model, the workload, and the reference cooling water flow rate, the controller calculates the predicted outlet temperature of the cooling zone.

[0017] In one implementation, this step specifically involves: taking the workload, the reference cooling water flow rate, and the updatable model parameters as inputs, and solving the online thermal model in a forward manner to obtain the predicted outlet temperature.

[0018] S6. Calculate the model residual: The controller calculates the model residual between the actual outlet temperature and the predicted outlet temperature.

[0019] S7. Adaptive calibration: The controller uses the model residuals to update the updatable model parameters in the online thermal model.

[0020] In one implementation, the updatable model parameters are used to characterize the effective heat transfer coefficient of the cooling region. The update step may employ a gradient descent algorithm or a least mean square algorithm to iteratively adjust the values ​​of the updatable model parameters based on the magnitude and polarity of the model residuals.

[0021] In a preferred embodiment, the method further includes a fault diagnosis step: the controller continuously monitors the workload and the actual outlet temperature. When the workload exceeds a preset power threshold, and the actual outlet temperature does not change accordingly or is lower than the predicted outlet temperature by a preset temperature difference threshold, the controller determines that a heat transfer path fault has occurred in the cooling area and issues an alarm.

[0022] In another preferred embodiment, the method further includes a global heat load adjustment step: The controller aggregates the heat dissipation power of all cooling zones to calculate the instantaneous total heat load of the system. In one embodiment, this aggregation is based on the reference cooling water flow rate, the actual outlet temperature, and the cooling medium inlet temperature of each cooling zone. The actual heat dissipation power of each cooling zone is calculated, and then all the actual heat dissipation powers are summed to obtain the instantaneous total heat load.

[0023] Based on the instantaneous total heat load, the controller adjusts the fan speed of the air-cooled surface cooler and the speed of the main circulating water pump of the cooling system to maintain the stability of the inlet temperature of the cooling medium.

[0024] Implementing this invention has the following beneficial effects: 1. This invention divides the cooling channel into multiple physical cooling zones by setting up a partitioned cooling backplate assembly, and equips each cooling zone with an independent micro-flow regulating valve and temperature sensor. This design allows the controller module to achieve partitioned and independent flow regulation and temperature monitoring according to the actual needs of the semiconductor devices corresponding to each zone, effectively solving the problem of uneven heat load on spliced ​​semiconductor devices and improving the temperature uniformity and control flexibility of the system.

[0025] 2. This invention employs a workload-based feedforward control method. The controller module acquires the workload in step S1, and in step S2, immediately calculates the required reference cooling water flow rate based on the online thermal model, then drives the regulating valve to execute in step S3. This feedforward regulation method can compensate for temperature deviations from the target before thermal disturbances occur, overcoming the inherent lag of traditional temperature feedback control and improving the response speed and stability of temperature control.

[0026] 3. The method of this invention includes an online model adaptation step. The controller module calculates the model residual between the actual outlet temperature and the model-predicted outlet temperature through steps S4 to S6, and iteratively updates the updatable model parameters in the online thermal model using this residual in step S7. This adaptive mechanism enables the control model to automatically track and compensate for slow changes in the system's physical heat transfer characteristics caused by factors such as flow channel fouling and aging of the heat transfer medium, ensuring that the cooling system maintains high-precision control performance during long-term operation. Attached Figure Description

[0027] Figure 1 This is a perspective view of the present invention; Figure 2 This is a schematic diagram of the semiconductor backplane of the present invention; Figure 3 This is a schematic diagram of the cooling cover plate of the present invention; Figure 4 for Figure 2 A magnified view of point A; Figure 5 for Figure 3 Enlarged view of B in the image; Figure 6 This is a schematic diagram of the method flow of the present invention.

[0028] In the diagram: 1. Semiconductor backplane; 2. Cooling cover; 3. Water inlet; 4. Water outlet; 5. Temperature sensor; 6. Micro-flow regulating valve; 7. Air-cooled surface cooler; 8. Main circulating water pump; 9. Controller; 10. Groove; 11. Sealing strip groove; 12. Protrusion; 13. Silicone sealing strip. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Please see the appendix Figure 1 -Appendix Figure 5 The cooling system provided by this invention can be used to implement a cooling method suitable for simulating infrared background in spliced ​​semiconductors.

[0031] The cooling system includes a partitioned cooling backplate assembly. The partitioned cooling backplate assembly includes a semiconductor backplate 1 with grooves 10 for water circulation and grooves 11 for sealing strips. A silicone sealing strip 13 is installed within the grooves 11. A cooling cover plate 2 also has grooves 10 and protrusions 12 that compress the silicone sealing strip 13, covering the grooves 10 for water circulation and the silicone sealing strip 13 to form a sealed cooling channel.

[0032] The water circulation groove 10 is physically divided into multiple sections. Each section spatially corresponds to one or a group of semiconductor devices mounted on the front side of the semiconductor backplane 1.

[0033] The cooling system also includes a micro-flow regulating valve 6 and a temperature sensor 5.

[0034] In one embodiment, a micro-flow regulating valve 6 is disposed at the inlet 3 of the cooling cover plate 2 or on the pipeline connected to the inlet 3, for regulating the flow rate of the incoming cooling medium. A temperature sensor 5 is disposed at the outlet 4 of the cooling cover plate 2 or on the pipeline connected to the outlet 4, for monitoring the temperature of the outgoing cooling medium.

[0035] The cooling system also includes a controller 9. The controller 9 is electrically or via data communication with all the micro-flow regulating valves 6 and temperature sensors 5, and is used to perform the various steps in the cooling method of the present invention.

[0036] The cooling system further includes a main circulating water pump 8 and an air-cooled surface cooler 7. The main circulating water pump 8 powers the circulation of the cooling medium throughout the cooling loop. The air-cooled surface cooler 7 provides global cooling to the cooled medium after it has passed through. In one embodiment, the fan speed of the air-cooled surface cooler 7 and the speed of the main circulating water pump 8 are regulated by a controller 9.

[0037] See attached document Figure 6 The present invention provides a method for simulating infrared background cooling of spliced ​​semiconductors, executed by a controller 9. In one embodiment, the method may include the following steps: Initialization: Controller 9 loads the initial online thermal model and initializes the updatable model parameters in the online thermal model.

[0038] S1, Input: The workload acquired by controller 9, and the inlet temperature of the cooling medium.

[0039] S2, Calculate flow rate: Controller 9 calculates the required baseline cooling water flow rate based on workload, cooling medium inlet temperature, online thermal model and preset target temperature.

[0040] S3, Feedforward Execution: Controller 9 drives the corresponding micro-flow regulating valve 6 to adjust the cooling water flow rate to the reference cooling water flow rate.

[0041] S4, Get Feedback: The controller 9 obtains the actual outlet temperature through the corresponding temperature sensor 5.

[0042] S5, Calculate and predict the outlet temperature: The controller 9 calculates the predicted outlet temperature based on the online thermal model, workload, and reference cooling water flow rate.

[0043] S6, Calculate model residuals: Controller 9 further calculates the model residuals between the actual outlet temperature and the predicted outlet temperature.

[0044] S7 Model Adaptation: Controller 9 uses model residuals to update updatable model parameters in the online thermal model for use in subsequent calculation cycles.

[0045] Global adjustment: In one embodiment, the controller 9 performs global heat load adjustment steps in parallel, sums up all actual heat dissipation power to calculate the instantaneous total heat load of the system, and adjusts the fan speed of the air-cooled surface cooler 7 and the speed of the main circulating water pump 8 accordingly.

[0046] Fault diagnosis: In one embodiment, the controller 9 performs fault diagnosis steps in parallel, monitoring the correspondence between the workload and the actual outlet temperature to determine whether a heat transfer path fault has occurred.

[0047] During initialization, controller 9 performs system preparation operations before the cooling method begins. In one embodiment, this initialization operation may include the following sub-steps: To load the target temperature, controller 9 reads and loads the corresponding preset target temperature from its internal non-volatile memory (such as Flash or EEPROM). .in This is a number. The preset target temperature... The value is loaded into the volatile memory (e.g., RAM) of the controller 9 as a target reference for the subsequent step S2 (calculating the flow rate).

[0048] Load initial model parameters; controller 9 sets and loads updatable model parameters for the online thermal model. initial value .in This refers to either time or the number of iterations. This is the updatable model parameter. The effective heat transfer coefficient, physically characterized. The initial value of this parameter at the initial moment. It is set to a preset value. In one embodiment, this preset value may be 1, representing an assumed initial, uncalibrated heat transfer efficiency. This will serve as the starting point for iterative updates in the subsequent step S7 (model adaptation).

[0049] The controller 9 reads and loads known physical constants required for subsequent thermodynamic calculations from non-volatile memory. In one embodiment, these physical constants include the density of the cooling medium (e.g., water). and specific heat capacity These constants are used in the subsequent S2 (calculate flow rate) and S6 (calculate residuals) steps to solve the online thermal model in either the forward or backward direction.

[0050] In step S1 (Acquire Input), controller 9 performs periodic data acquisition to obtain the real-time input required for performing feedforward control and model calibration. In one embodiment, this step may include the following sub-steps: The workload is obtained by controller 9. Workload This is an electrical signal characterizing the thermal power that the corresponding semiconductor device will generate. In one embodiment, controller 9 is connected to an external host computer (e.g., the host system of an infrared background simulator) via its communication interface (e.g., an EtherCAT bus or CAN bus interface). When the host computer issues a power command to the corresponding semiconductor device, it simultaneously displays the value of this power command. (For example, in W) is sent to controller 9. Controller 9 receives and stores this. The value is used in step S2 (calculate flow rate).

[0051] The controller 9 obtains the inlet temperature of the cooling medium. In one embodiment, a global inlet temperature sensor 5 is installed on the main water supply pipe after the main circulating water pump 8 of the cooling system and before it enters the partitioned cooling backplate assembly. The controller 9 reads the measured value of the inlet temperature sensor 5 at a preset sampling frequency (e.g., 1 Hz) through its analog or digital input channel and uses it as... Stored in memory for use in steps S2 (calculate flow rate) and S6 (calculate residual).

[0052] In step S2 (calculating the reference cooling water flow rate), controller 9 performs the core calculations of feedforward control to determine how to maintain the target temperature. Required reference cooling water flow rate In one embodiment, this step may include the following sub-steps: The online thermal model is invoked, and controller 9 invokes it as (numbered as...). The online thermal model is maintained. In one embodiment, the online thermal model is based on the principle of thermodynamic equilibrium, and its simplified form can be expressed as: ; in: This represents the current time or iteration number. To obtain the workload; This refers to the flow rate of the cooling water passing through this area; This refers to the model outlet temperature of this region in the online thermal model (a theoretically calculated variable). To obtain the inlet temperature of the cooling medium; and The density and specific heat capacity of the cooling medium being loaded; These are the initialized, iteratively updated model parameters in subsequent S7 (model adaptation). As before, The effective heat transfer coefficient, which is physically characterized.

[0053] The controller 9 performs an inverse model solution, specifically an inverse solution of the online thermal model. The objective of this step is to ensure that the controller is functioning correctly under the load. At that time, its outlet temperature Able to stably load the preset target temperature within the target temperature range. .

[0054] Calculate the baseline flow rate, and controller 9 will... , , And the current Substitute the known inputs into the thermal model formula and solve. .at this time This is the required reference cooling water flow rate. : ; The controller 9 will store the baseline flow rate and calculate it. The values ​​are stored for use in the S3 (feedforward execution) step and the subsequent S6 (residual calculation) step.

[0055] In step S3 (feedforward execution), controller 9 uses the calculation results obtained in step S2 (calculate flow rate) to perform the calculation. This translates into physical control actions to adjust the actual flow rate of the cooling medium. In one embodiment, this step may include the following sub-steps: The controller 9 obtains the calculated reference cooling water flow rate. .

[0056] Query the valve characteristic curve, controller 9 according to The value is used to query the internally stored control characteristic curve or calibration data table corresponding to the micro-flow regulating valve 6. In one embodiment, the control characteristic curve describes the nonlinear or linear correspondence between the flow rate value (e.g., in L / min) and the drive signal of the micro-flow regulating valve 6 (e.g., a voltage signal of 0-10V or a current signal of 4-20mA). For obtaining this control characteristic curve, those skilled in the art can use a standard flowmeter to pre-calibrate the micro-flow regulating valve 6, which is a well-known technique in the field and will not be elaborated further here.

[0057] The flow rate is converted into a drive signal, and the controller 9, based on the query result, sets the reference cooling water flow rate. This is converted into a corresponding drive signal (e.g., a specific voltage or current value).

[0058] The controller 9 sends a drive signal to the regulating valve, and through its output interface (e.g., analog output (AO) channel or digital bus interface), the corresponding drive signal is converted and sent to the micro-flow regulating valve 6 installed on the inlet 3 connecting pipeline.

[0059] The regulating valve performs the regulating action, and the micro-flow regulating valve 6 responds to the drive signal, adjusting its valve core opening or other flow control mechanisms to adjust the actual flow rate of the cooling medium to the reference cooling water flow rate. .

[0060] This step S3 involves acquiring the workload. The adjustment action of the micro-flow regulating valve 6 is executed immediately after the load. The corresponding heat is fully accumulated and conducted to the outlet 4 before it is completed, thus achieving feedforward suppression of thermal disturbance and avoiding the inherent lag of traditional temperature feedback control.

[0061] In step S4 (obtaining feedback), controller 9 performs feedback data acquisition to obtain the data in the current calculation cycle (denoted as...). The physical measurement temperature. In one embodiment, this step may include the following sub-steps: Establish a sensor connection. The controller 9 connects to the temperature sensor 5 installed on the connection pipe of the outlet 4 via its input interface (e.g., analog input (AI) channel or digital bus interface).

[0062] Triggering timed sampling, after step S3 (feedforward execution), controller 9 performs sampling in the current calculation cycle. A preset sampling time triggers the reading operation of temperature sensor 5. In one embodiment, the setting of this preset sampling time can take into account the fluid transit time and the heat conduction time constant to ensure that the read temperature value reflects the reference cooling water flow rate adjusted in S3. and the acquired workload Thermal state under combined action.

[0063] To obtain the actual outlet temperature, controller 9 reads the instantaneous measurement value from temperature sensor 5 and records this value as the actual outlet temperature. .Should It is the model outlet temperature The actual corresponding measurement value in the physical world.

[0064] Store the actual outlet temperature; controller 9 will store the actual outlet temperature. It is stored in its volatile memory for use in the subsequent S6 (calculate the residual) step.

[0065] In step S5 (calculating the predicted outlet temperature), controller 9 performs a forward solution of the online thermal model to calculate the predicted outlet temperature in the current calculation cycle k. In one embodiment, this step may include the following sub-steps: The controller 9 performs a forward solution to the online thermal model to calculate the predicted outlet temperature in the current calculation cycle k. The controller 9 will calculate and predict the outlet temperature, and then acquire the workload. The storage reference cooling water flow rate The obtained inlet temperature of the cooling medium and the current updatable model parameters. Substituting the publicly available online thermal model formula, the model outlet temperature is solved in the forward direction. The result of this solution is the predicted outlet temperature. : ; In step S6 (calculating model residuals), controller 9 performs calculations to quantify the deviation between the predicted values ​​of the online thermal model and the actual measured values ​​of the physical system. In one embodiment, this step may include the following sub-steps: The model residuals are calculated, and controller 9 further calculates the actual outlet temperature obtained in S4 (get feedback). The predicted outlet temperature calculated in S5 (calculate the predicted outlet temperature) The difference between them. This difference is the model residual. : ; Model residuals The online thermal model is physically characterized (by the current...) (Definition) The estimation bias of the actual physical heat transfer characteristics of a substance.

[0066] Store the model residuals; controller 9 will calculate the model residuals. Store it in its volatile memory for use in the subsequent S7 (model adaptation) step.

[0067] In step S7 (model adaptive update), controller 9 uses the model residuals obtained in S6 (calculate residuals). For updatable model parameters in the online thermal model Perform iterative updates. This step enables the online thermal model to automatically adapt to drifts in the effective heat transfer coefficient caused by fouling, aging, or changes in operating conditions. In one embodiment, this step may include the following sub-steps: Obtain the model residuals; controller 9 obtains the current calculation cycle. Model residuals .

[0068] Obtain the current model parameters; controller 9 obtains the current calculation cycle. Updatable model parameters used .

[0069] Calculate the new model parameters, and controller 9 executes an iterative update algorithm to calculate the parameters for the next computation cycle. (New, updatable model parameters) In one embodiment, the update algorithm may be implemented based on the principles of the Least Mean Square (LMS) algorithm or the gradient descent algorithm. For example, controller 9 may calculate according to the update law shown below. : ; in: It is a preset update step size (also known as the learning rate), which is a constant greater than 0.

[0070] Define the update step size, and then update the step size. The specific values ​​to be selected can be weighed by those skilled in the art based on experimental data and simulations, balancing the convergence speed and stability of the algorithm. This is a well-known technique in the field and will not be elaborated here.

[0071] The controller 9 stores the new model parameters and calculates the new, updatable model parameters. Store it in its volatile memory, replacing the original one. Values ​​are used for the next calculation cycle. Used when performing steps S2 (calculate flow rate) and S6 (calculate residual).

[0072] In a preferred embodiment, the method may further include a global heat load adjustment step, which adjusts the global cooling capacity of the cooling system according to the instantaneous total heat load of the zoned cooling backplate assembly (10) to dynamically maintain the cooling medium inlet temperature. The stability. In one embodiment, this step may include the following sub-steps: Controller 9 obtains the global calculation input and acquires the stored reference cooling water flow rate. The actual outlet temperature obtained and the obtained inlet temperature of the cooling medium. .

[0073] To calculate the heat dissipation power of the calculated area, controller 9 calculates the current calculation cycle based on the following thermodynamic formula. Next, the The actual heat dissipation power of each : ; Calculate the instantaneous total heat load; controller 9 controls all... The actual heat dissipation power of each Accumulate (where From 1 to , (total number), to obtain the system's current calculation cycle. Instantaneous total heat load : ; By setting the global adjustment input, controller 9 will input the instantaneous total heat load. As an adjustment input, it is used to control the global refrigeration equipment of the cooling system, such as the air-cooled surface cooler 7 or the main circulating water pump 8.

[0074] Performing global feedforward regulation, in one embodiment, controller 9 can... As a feedforward control signal. For example, controller 9 can maintain a signal that controls the instantaneous total heat load. A control law or lookup table mapped to the fan speed (operating frequency of the main circulating water pump 8) of the air-cooled surface cooler 7. When When the load increases, controller 9 correspondingly increases the fan speed or water pump frequency to remove more heat, thereby actively maintaining the inlet temperature of the cooling medium when the heat load fluctuates. Stability.

[0075] In a preferred embodiment, the method may further include a heat transfer fault diagnosis step.

[0076] In one embodiment, this step determines whether a heat transfer path failure has occurred by monitoring the logical relationship between the workload and temperature feedback. This step may include the following sub-steps: The workload obtained by controller 9 Actual outlet temperature and predicted outlet temperature .

[0077] Controller 9 reads a power threshold from its non-volatile memory. and a temperature difference threshold .

[0078] Controller 9 performs fault logic judgment. In one embodiment, this logic judgment is based on the simultaneous satisfaction of the following two conditions: (1) (That is, the current workload is significantly greater than a preset baseline value). (2) (That is, the actual measured temperature is significantly lower than the temperature predicted by the model). If the logical judgment is true, controller 9 determines that a heat transfer path failure has occurred.

[0079] This heat transfer path failure can physically correspond to the failure or improper installation of the heat transfer medium (e.g., thermal grease) between the semiconductor device (heat source) and the semiconductor backplane 1, resulting in increased workload. The generated heat cannot be effectively transferred to the flow channel, thus reducing the actual outlet temperature. Much lower than in S602 (calculation of predicted outlet temperature) based on Predicted .

[0080] After determining that a heat transfer path failure has occurred, controller 9 sends an alarm signal.

[0081] In another preferred embodiment, the heat transfer fault diagnosis step utilizes the continuously updated updatable model parameters in S7 (model adaptation). The heat transfer health status is monitored online. This step may include the following sub-steps: Controller 9 acquires and updates the updatable model parameters for the next calculation cycle. As before, this parameter Physically, it characterizes the effective heat transfer coefficient.

[0082] Controller 9 reads a value (numbered as) from its non-volatile memory. Preset lower threshold value for heat transfer coefficient .Should The value represents the minimum effective heat transfer coefficient that should be present under normal operating conditions (e.g., clean, unblocked flow channels).

[0083] Controller 9 will acquire updatable model parameters The current value, compared with the read lower limit threshold of the heat transfer coefficient. Compare them.

[0084] In one embodiment, if controller 9 determines If the condition is met, controller 9 determines that a heat transfer fault has occurred.

[0085] A heat transfer failure can be specifically indicated by scaling or blockage in the internal flow channels (e.g., serpentine channel 13), resulting in a decrease in its effective heat transfer coefficient (as determined by...). The (characteristic) continued to decline and fell below the lower threshold. .

[0086] Upon detecting a heat transfer fault, controller 9 can send an alarm signal or fault status code to an external human-machine interface (HMI) or main control system via its digital output channel or communication interface. This alarm signal is used to prompt maintenance personnel to inspect or clean the corresponding flow path.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for mosaic semiconductor infrared background simulation cooling, applied to a cooling system with a partitioned cooling backplane, said cooling system comprising cooling areas, micro-flow regulating valves (6) and temperature sensors (5) corresponding to said cooling areas respectively, and a controller (9); characterized in that: The method comprises the following steps: S1, obtaining the working load of at least one cooling area and the inlet temperature of the cooling medium; S2, calculating the reference cooling water flow rate required by the cooling area based on the working load, the inlet temperature of the cooling medium, an online thermal model and a preset target temperature of the cooling area, wherein the online thermal model comprises an updateable model parameter; S3, controlling the micro-flow regulating valve (6) corresponding to the cooling area to adjust the cooling water flow rate to the reference cooling water flow rate; S4, obtaining the actual outlet temperature of the cooling area through the temperature sensor (5) corresponding to the cooling area; S5, calculating the predicted outlet temperature of the cooling area based on the online thermal model, the working load and the reference cooling water flow rate; S6, calculating the model residual between the actual outlet temperature and the predicted outlet temperature; S7, updating the updateable model parameter in the online thermal model using the model residual.

2. The method according to claim 1, wherein the method is suitable for mosaic semiconductor infrared background simulation cooling, and characterized in that: Further comprising: monitoring the working load and the actual outlet temperature; when the working load is greater than a preset power threshold and the actual outlet temperature does not change correspondingly or is lower than the predicted outlet temperature by a preset temperature difference threshold, determining that the cooling area has a heat transfer path failure and issuing an alarm.

3. The method according to claim 1, wherein the method is suitable for mosaic semiconductor infrared background simulation cooling, characterized in that: Further comprising: summing up the heat dissipation power of all cooling areas to calculate the instantaneous total heat load of the system; based on the instantaneous total heat load, adjusting the fan rotating speed of the air-cooled surface cooler (7) and the rotating speed of the main circulating water pump (8) of the cooling system.

4. The method according to claim 3, wherein the method is suitable for mosaic semiconductor infrared background simulation cooling. The step of summing up the heat dissipation power of all cooling areas comprises: calculating the actual heat dissipation power of each cooling area based on the reference cooling water flow rate, the actual outlet temperature and the inlet temperature of the cooling medium of each cooling area; accumulating all the actual heat dissipation powers to obtain the instantaneous total heat load.

5. The method for simulating infrared background cooling of spliced ​​semiconductors according to claim 1, characterized in that: The updateable model parameter represents the effective heat transfer coefficient of the cooling area.

6. The method of claim 1, wherein the method is suitable for mosaic semiconductor infrared background simulation cooling. The S7 step specifically comprises: using a gradient descent algorithm or a least mean square algorithm to iteratively adjust the numerical value of the updateable model parameter according to the amplitude and polarity of the model residual.

7. The method of claim 1, wherein the method is suitable for mosaic semiconductor infrared background simulation cooling. The S2 step specifically comprises: based on the working load, the updateable model parameter, the target temperature and the inlet temperature of the cooling medium, inversely solving the online thermal model to obtain the reference cooling water flow rate.

8. The method of claim 1, wherein the method is suitable for mosaic semiconductor infrared background simulation cooling. In the S5 step, the step of calculating the predicted outlet temperature comprises: forward solving the online thermal model to obtain the predicted outlet temperature by taking the working load, the reference cooling water flow rate and the updateable model parameter as inputs.

9. A method for simulating infrared background cooling of spliced ​​semiconductors according to claim 1, characterized in that: The partition cooling backboard comprises: a semiconductor backboard (1) having a sealing strip groove (11) formed therein; a silica gel sealing strip (13) mounted in the sealing strip groove (11); a cooling cover plate (2) having a protrusion (12) for extruding the silica gel sealing strip (13); the semiconductor backboard (1) and the cooling cover plate (2) each have a water circulation groove (10) formed therein; The micro-flow regulating valve (6) is arranged on the pipeline connected with the water inlet (3) of the cooling cover plate (2); The temperature sensor (5) is arranged on the pipeline connected with the water outlet (4) of the cooling cover plate (2).

10. The method according to claim 9, wherein the method is suitable for mosaic semiconductor infrared background simulation cooling. The cooling area is a plurality of sub-zones physically divided by the groove (10) of the water circulation on the back surface of the semiconductor back plate (1) and independently controllable by the micro-flow regulating valve (6).

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