Sensor-based integrated circuit layout optimization method and device

Abstract

This invention relates to the field of integrated circuit technology, specifically including a sensor-based integrated circuit layout optimization method and apparatus. The method includes: setting an initial circuit layout network based on an integrated circuit substrate; determining candidate circuit layout combinations by combining thermal isolation wiring constraints and electromagnetic isolation wiring constraints; inputting the results into a multi-objective optimization engine for Pareto front search; and determining the globally optimal circuit layout combination. This solves the technical problems of inaccurate identification of multi-field coupling amplification regions, lack of targeted wiring constraints, and limited reliability of sensor-based integrated circuits. It achieves the technical effect of combining thermal isolation constraints of thermal feedback reinforcement loops with electromagnetic isolation constraints of electromagnetic resonant loops, introducing a multi-objective optimization engine to conduct Pareto front search, and simultaneously generating non-dominated solution sets for thermoelectric coupling, magnetomechanical coupling, and electromechanical coupling, effectively controlling the adverse effects of multi-physics field interactions on circuit performance.

Description

Technical Field

[0001] This invention relates to the field of integrated circuit technology, and specifically to a sensor-based method and apparatus for optimizing integrated circuit layout. Background Technology

[0002] The performance of integrated circuits depends not only on the accuracy of the sensor itself, but also on the rationality of the circuit layout. Current optimization of sensor-based integrated circuit layout is one-sided, often setting wiring rules solely for thermal isolation or electromagnetic isolation. This leads to performance degradation in actual operation due to inter-field interactions, such as thermal expansion causing changes in electrode spacing that affect electric field stability, and mechanical deformation inducing parasitic capacitance fluctuations that interfere with the sensing signal. In addition, the layout optimization process lacks quantitative analysis of the coupling strength between the sensor and adjacent functional units, making it impossible to accurately identify multi-field coupling amplification areas. This results in a lack of specificity in the wiring constraints, further reducing the effectiveness of layout optimization.

[0003] In summary, existing technologies suffer from technical problems such as the inability to accurately identify multi-field coupling amplification regions, a lack of targeted wiring constraints, and limited reliability of sensor-based integrated circuits. Summary of the Invention

[0004] This application provides a sensor-based integrated circuit layout optimization method and apparatus, aiming to solve the technical problems in the prior art, such as the inability to accurately identify multi-field coupling amplification regions, the lack of targeted wiring constraint settings, and the limited reliability of sensor-based integrated circuits.

[0005] In view of the above problems, the technical solution to achieve the present application is as follows:

[0006] In a first aspect, this application provides a sensor-based integrated circuit layout optimization method, wherein the method includes: setting an initial circuit layout network based on an integrated circuit substrate; determining candidate circuit layout combinations based on the initial circuit layout network, combined with thermal isolation wiring constraints under a thermal feedback enhancement loop and electromagnetic isolation wiring constraints under an electromagnetic resonant loop; inputting the candidate circuit layout combinations into a multi-objective optimization engine for Pareto front search to obtain thermoelectric coupling non-dominated solution sets, magnetomechanical coupling non-dominated solution sets, and electromechanical coupling non-dominated solution sets, and determining the globally optimal circuit layout combination.

[0007] Preferably, the influence of temperature distribution on electric field is simulated by finite element method, the effect of carrier mobility change and leakage current increase caused by temperature rise is analyzed, the electrode spacing change caused by thermal expansion is dynamically optimized, and thermal through-hole array and thermal conductivity isolation groove are introduced to determine the non-dominated solution set of thermoelectric coupling.

[0008] Preferably, for magnetically sensitive components including TMR and Hall elements, the mechanical deformation caused by magnetostriction is analyzed, the orientation and layout position on the integrated circuit substrate are optimized, and the non-dominated solution set of magnetomechanical coupling is determined.

[0009] Preferably, for electrical sensitive components including electrostatic sensors and MEMS capacitive sensing units, the electrode spacing and the stiffness of the support structure are adjusted to determine the electromechanical coupling non-dominated solution set; at the same time, a coplanar shielding structure is introduced in the wiring path, which is used to suppress parasitic capacitance fluctuations induced by mechanical deformation.

[0010] Preferably, temperature field data, electric field data, and magnetic field data are mapped to the IC physical coordinate system, and the coupling strength between adjacent sensors is set. The coupling strength between adjacent sensors is used to quantify the spatial correlation between electrical crosstalk and magnetic interference. Based on the IC physical coordinate system and the coupling strength between adjacent sensors, mutual information nonlinear coupling analysis is performed with the temperature field and electric field coupling amplification partition, and thermal isolation wiring constraints under the thermal feedback enhancement loop are set.

[0011] Preferably, thermal amplification coupling analysis based on physical coupling effect is performed under layout association to construct the heat conduction path between the sensor metal trace and the adjacent functional unit, determine the thermal resistance matrix, and predict the temperature rise gradient by combining dynamic power consumption waveform. When there is a coherent match between the local temperature rise rate and the main frequency of electric field fluctuation, the temperature field and electric field coupling amplification partition and the thermal feedback enhancement loop are determined.

[0012] Preferably, based on the IC physical coordinate system, the coupling strength between adjacent sensors, and the magnetic field and electric field coupling amplification partition, a mutual information nonlinear coupling analysis is performed to set electromagnetic isolation wiring constraints under the electromagnetic resonant circuit.

[0013] Preferably, electromagnetic amplification coupling analysis based on physical coupling effect is performed under layout correlation to construct the electromagnetic conduction path between sensor metal traces and adjacent functional units, determine the mutual inductance matrix, and predict the magnetic field strength gradient by combining dynamic power consumption waveform. When the local magnetic field change rate and the electric field fluctuation main frequency have a coherent match, the magnetic field and electric field coupling amplification partition and the electromagnetic resonant circuit are determined.

[0014] Preferably, on the integrated circuit substrate, a physical sensor array covering the IC core area is used to collect temperature field data, electric field data, and magnetic field data of the integrated circuit substrate during simulated operation; based on the temperature field data, electric field data, and magnetic field data, the dominant frequency of temperature fluctuation, electric field coupling strength, and magnetic field coupling strength are extracted, and inter-field correlation feature analysis is performed to identify the physical coupling effect that affects the layout performance.

[0015] In a second aspect, this application provides a sensor-based integrated circuit layout optimization device, comprising: an initial circuit layout network setting module for setting an initial circuit layout network based on an integrated circuit substrate; a candidate circuit layout combination determination module for determining candidate circuit layout combinations based on the initial circuit layout network and in conjunction with thermal isolation wiring constraints under a thermal feedback enhancement loop and electromagnetic isolation wiring constraints under an electromagnetic resonant loop; and a search module for inputting the candidate circuit layout combinations into a multi-objective optimization engine for Pareto front search to obtain thermoelectric coupling non-dominated solution sets, magnetomechanical coupling non-dominated solution sets, and electromechanical coupling non-dominated solution sets, thereby determining the globally optimal circuit layout combination.

[0016] In summary, one or more technical solutions provided in this application achieve the technical effect of combining the thermal isolation constraint of the thermal feedback enhancement loop with the electromagnetic isolation constraint of the electromagnetic resonant loop, introducing a multi-objective optimization engine to carry out Pareto front search, and simultaneously generating non-dominated solution sets of thermo-electric coupling, magneto-mechanical coupling, and electro-mechanical coupling, effectively controlling the adverse effects of multi-physics field interaction on circuit performance, and improving the operational stability of sensor-based integrated circuits. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the sensor-based integrated circuit layout optimization method is provided for this application.

[0018] Figure 2 This application provides a schematic diagram of the structure of a sensor-based integrated circuit layout optimization device.

[0019] Explanation of reference numerals in the attached diagram: Initial circuit layout network setting module M100, candidate circuit layout combination determination module M200, and search module M300. Detailed Implementation

[0020] Example 1: The present application will be described in detail below with reference to the accompanying drawings, as follows... Figure 1 As shown, this application provides a sensor-based integrated circuit layout optimization method, wherein the method includes:

[0021] S1: Based on the integrated circuit substrate, set an initial circuit layout network; S2: Based on the initial circuit layout network, combined with the thermal isolation wiring constraints under the thermal feedback enhancement loop and the electromagnetic isolation wiring constraints under the electromagnetic resonant loop, determine the candidate circuit layout combination.

[0022] Specifically, the integrated circuit substrate is the physical support structure for integrated circuits, used to fix and connect various electronic components, including sensors and transistors. It is usually made of insulating materials and has good thermal conductivity and electrical insulation. The initial circuit layout network is the preliminary circuit layout designed on the integrated circuit substrate, including the position of components and the initial routing of connecting lines. The initial layout network is the basis for subsequent optimization and is usually set according to functional requirements and basic electrical rules. The thermal feedback reinforcement loop is used to monitor and regulate the temperature distribution in the integrated circuit. Through the thermal feedback reinforcement loop, the thermal management strategy of the circuit is dynamically adjusted to reduce the impact of thermal expansion on circuit performance. Thermal isolation wiring constraints refer to the wiring rules set for thermal management, which aim to reduce the propagation and accumulation of heat in the circuit. Furthermore, by setting thermal isolation grooves and using low thermal conductivity materials, thermal coupling is effectively reduced.

[0023] An electromagnetic resonant circuit refers to a circuit structure used to monitor and regulate the distribution of electromagnetic fields. Through an electromagnetic resonant circuit, the distribution of electromagnetic fields can be optimized and electromagnetic interference can be reduced. Electromagnetic isolation wiring constraints are wiring rules set for electromagnetic compatibility, aiming to reduce mutual interference between electromagnetic fields. Furthermore, by setting shielding layers and optimizing wiring paths, electromagnetic coupling can be effectively reduced. Candidate circuit layout combinations are a series of possible circuit layout schemes generated under the condition of satisfying thermal isolation and electromagnetic isolation constraints, used to further screen out the optimal layout scheme.

[0024] Execution steps: An initial circuit layout network is set up on the integrated circuit substrate according to functional requirements and basic electrical rules. Combining the thermal isolation wiring constraints under the thermal feedback enhancement loop and the electromagnetic isolation wiring constraints under the electromagnetic resonant loop, the initial layout network is evaluated and adjusted to generate candidate circuit layout combinations that satisfy the thermal and electromagnetic isolation constraints. In the above steps, by combining thermal and electromagnetic isolation constraints, performance degradation caused by thermal expansion and electromagnetic interference can be effectively reduced. Simultaneously, by optimizing the orientation and layout of magnetically sensitive components, mechanical deformation caused by magnetostriction can be reduced, minimizing interference with sensing signals and ensuring the performance stability of candidate circuit layout combinations under multi-physics interaction.

[0025] S3: Input the candidate circuit layout combination into the multi-objective optimization engine to perform Pareto front search, obtain the non-dominated solution sets of thermoelectric coupling, magnetomechanical coupling, and electromechanical coupling, and determine the globally optimal circuit layout combination.

[0026] Specifically, a multi-objective optimization engine is an algorithmic model used to solve multi-objective optimization problems. It can consider multiple objective functions simultaneously and find the best trade-off solution among these objectives. Common multi-objective optimization algorithms include non-dominated sorting genetic algorithms and multi-objective particle swarm optimization algorithms. The Pareto front refers to the set of solutions in a multi-objective optimization problem that cannot be improved by improving the value of one objective function without harming the values ​​of other objective functions. Specifically, Pareto front search is the process of finding these non-dominated solutions through specific algorithms.

[0027] Thermoelectric coupling non-dominated solution set refers to a set of non-dominated solutions obtained through multi-objective optimization under thermoelectric coupling effects, representing the optimal trade-off between different performance indicators in thermoelectric coupling scenarios. Magnetomechanical coupling non-dominated solution set refers to a set of non-dominated solutions obtained through multi-objective optimization under magnetomechanical coupling effects, representing the optimal trade-off between different performance indicators in magnetomechanical coupling scenarios. Electromechanical coupling non-dominated solution set refers to a set of non-dominated solutions obtained through multi-objective optimization under electromechanical coupling effects, representing the optimal trade-off between different performance indicators in electromechanical coupling scenarios. The globally optimal circuit layout combination is selected from the thermoelectric coupling non-dominated solution set, magnetomechanical coupling non-dominated solution set, and electromechanical coupling non-dominated solution set, representing the circuit layout scheme with the best performance under multi-physics field interactions, ensuring that integrated circuits maintain optimal performance in complex working environments.

[0028] Execution steps: A comprehensive analysis of the non-dominated solution sets of thermoelectric coupling, magnetomechanical coupling, and electromechanical coupling is conducted to determine the globally optimal circuit layout combination. The Pareto front search algorithm is used to improve performance optimization under thermoelectric coupling, magnetomechanical coupling, and electromechanical coupling effects, significantly reducing performance degradation caused by multi-physics coupling, such as the impact of thermal expansion on electrode spacing changes on electric field stability and mechanical deformation-induced parasitic capacitance fluctuations interfering with sensing signals. The globally optimal circuit layout combination is determined through this process. In the above steps, a multi-objective optimization algorithm comprehensively weighs the performance indicators under different physical field coupling effects to determine the optimal circuit layout scheme.

[0029] Furthermore, to obtain the thermoelectric coupling non-dominated solution set, the method of this application includes:

[0030] The influence of temperature distribution on the electric field is simulated by finite element method. The effects of temperature rise on carrier mobility change and leakage current increase are analyzed. The electrode spacing change caused by thermal expansion is dynamically optimized. Thermal through-hole array and thermal conductivity isolation groove are introduced to determine the non-dominated solution set of thermoelectric coupling.

[0031] Specifically, in integrated circuit design, finite element simulation can be used to simulate the interaction of physical fields such as temperature distribution and electric field distribution, and analyze the impact of different design parameters on circuit performance. In semiconductor materials, carrier mobility is a key parameter affecting circuit performance. Carriers are mainly divided into electrons and holes. Furthermore, increased temperature leads to a decrease in carrier mobility, thereby affecting the electrical performance of the circuit. The leakage current increase effect refers to the phenomenon that leakage current, under non-ideal conditions, flows through paths it should not. Specifically, as leakage current increases, static power consumption increases significantly, exacerbating the heating of semiconductor devices, forming a positive feedback thermal cycle, and further limiting the operational stability and reliability of integrated circuits.

[0032] In integrated circuits, thermal expansion causes changes in electrode spacing, thereby affecting the electric field distribution and circuit performance. Thermal vias are structures used to improve heat dissipation efficiency. Multiple thermal vias are typically set in the integrated circuit substrate to form a thermal via array to enhance heat conduction and dissipation. Thermal conductivity isolation trenches are structures used to reduce heat conduction. By setting isolation trenches made of low thermal conductivity material in the substrate, the propagation of heat between different areas can be effectively reduced. Thermoelectric coupling non-dominated solution set refers to a set of non-dominated solutions obtained through multi-objective optimization under the thermoelectric coupling effect. It represents the optimal trade-off between different performance indicators in the thermoelectric coupling scenario.

[0033] Execution Steps: The temperature distribution within the integrated circuit is simulated using finite element method (FEM) simulation, and the impact of these temperature changes on the electric field is analyzed. Specifically, the simulation calculates the changes in carrier mobility and leakage current caused by temperature rise. These effects affect the circuit's electrical performance. Simultaneously, the electrode spacing changes caused by thermal expansion are dynamically optimized to reduce electric field instability due to thermal expansion. Furthermore, thermal via arrays and thermally conductive isolation trenches are introduced to enhance heat dissipation and reduce thermal coupling. These measures generate a thermo-electric coupling non-dominated solution set, providing a foundation for subsequent global optimization. Through FEM simulation and dynamic optimization, the changes in carrier mobility caused by temperature rise are reduced, and the leakage current increase effect is mitigated. Simultaneously, the local temperature rise rate is reduced by introducing thermal via arrays and thermally conductive isolation trenches, thereby reducing the impact of electrode spacing changes caused by thermal expansion on electric field stability. In these steps, accurate simulation and optimization reduce the negative impact of thermo-electric coupling effects on circuit performance.

[0034] Furthermore, to obtain the non-dominated solution set of magnetomechanical coupling, the method of this application includes:

[0035] For magnetically sensitive components including TMR and Hall elements, the mechanical deformation caused by magnetostriction is analyzed, the orientation and layout position on the integrated circuit substrate are optimized, and the non-dominated solution set of magnetomechanical coupling is determined.

[0036] Specifically, TMR (Tunneling Magnetoresistance) elements are magnetically sensitive elements based on the quantum tunneling effect, whose resistance changes with the external magnetic field. TMR elements are widely used in high-sensitivity magnetic field detection. Hall elements are magnetically sensitive elements based on the Hall effect, capable of generating a Hall voltage proportional to the magnetic field strength under the influence of a magnetic field. Hall elements are widely used in magnetic field measurement and magnetic field sensing. Magnetic sensitive components are highly sensitive to changes in magnetic fields, including TMR elements and Hall elements. Magnetostriction refers to the phenomenon of mechanical deformation of magnetic materials under the influence of a magnetic field, which affects the performance and layout of magnetically sensitive components. Mechanical deformation refers to the shape change of a material under the influence of external force or magnetic field. In magnetically sensitive components, mechanical deformation may lead to changes in sensor performance. The non-dominated solution set of magnetomechanical coupling refers to a set of non-dominated solutions obtained through multi-objective optimization under the magnetomechanical coupling effect, representing the optimal trade-off between different performance indicators in the magnetomechanical coupling scenario.

[0037] Execution Steps: The magnetically sensitive components, including TMR elements and Hall elements, are analyzed. Specifically, the mechanical deformation caused by the magnetostrictive effect is analyzed. Finite element simulation and other methods are used to simulate the impact of magnetic field changes on the mechanical deformation of these components. Based on the simulation results, the orientation and layout of these magnetically sensitive components on the integrated circuit substrate are optimized. Ideally, regions with strong alternating magnetic fields are avoided to reduce structural resonance and sensitivity degradation caused by magnetic field changes, thereby reducing the negative impact of mechanical deformation on performance. A magnetomechanical coupling non-dominated solution set is generated. Through finite element simulation and optimization, the mechanical deformation caused by the magnetostrictive effect is reduced, thus reducing interference with the sensing signal. Specifically, the mechanical deformation caused by the magnetostrictive effect is reduced by optimizing the orientation and layout of the TMR and Hall elements. The optimized layout reduces signal interference from the magnetically sensitive components, significantly improving the accuracy and reliability of the sensor. A magnetomechanical coupling non-dominated solution set is generated. In the above steps, accurate simulation and optimization reduce the negative impact of the magnetomechanical coupling effect on circuit performance.

[0038] Furthermore, to obtain the electromechanical coupling non-dominated solution set, the method of this application includes:

[0039] For electrical sensitive components including electrostatic sensors and MEMS capacitive sensing units, the electrode spacing and support structure stiffness are adjusted to determine the electromechanical coupling non-dominated solution set; at the same time, a coplanar shielding structure is introduced in the wiring path to suppress parasitic capacitance fluctuations induced by mechanical deformation.

[0040] Specifically, an electrostatic sensor is a sensor that uses the principle of electrostatics to detect changes in an electric field, typically by measuring changes in capacitance between electrodes to detect the electric field strength; a MEMS (Micro-Electro-Mechanical System) capacitive sensing unit is a sensor based on MEMS technology that detects physical quantities such as pressure, acceleration, and displacement by measuring changes in capacitance; electrical sensitive components are highly sensitive to changes in the electric field, including electrostatic sensors and MEMS capacitive sensing units, and are used to detect and respond to changes in the electric field.

[0041] Electrode spacing refers to the distance between two electrodes. In capacitive sensors, changes in electrode spacing affect the capacitance value, thus affecting the sensor's sensitivity and accuracy. Support structure stiffness refers to the ability of the support structure to resist deformation. In MEMS capacitive sensing units, the stiffness of the support structure affects the sensor's stability and accuracy. Electromechanical coupling non-dominated solution set refers to a set of non-dominated solutions obtained through multi-objective optimization under electromechanical coupling effects, representing the optimal trade-off between different performance indicators in electromechanical coupling scenarios. Coplanar shielding structures are shielding designs used to suppress parasitic capacitance fluctuations, usually introduced in the wiring path to reduce the impact of mechanical deformation on the capacitance value.

[0042] Execution Steps: The electrical sensing components, including electrostatic sensors and MEMS capacitive sensing units, are analyzed. Specifically, the electrode spacing and the stiffness of the support structure are adjusted. The impact of mechanical deformation on capacitance is simulated using finite element simulation and other methods, and the electrode spacing and support structure stiffness are optimized to reduce parasitic capacitance fluctuations. Simultaneously, a coplanar shielding structure is introduced into the wiring path to further suppress parasitic capacitance fluctuations induced by mechanical deformation. Through these optimization measures, an electromechanical coupling non-dominated solution set is generated, providing a foundation for subsequent global optimization. By reducing parasitic capacitance fluctuations caused by packaging stress or vibration through finite element simulation and optimization, the accuracy and stability of the sensor are improved. Specifically, adjusting the electrode spacing and support structure stiffness reduces parasitic capacitance fluctuations caused by mechanical deformation; introducing a coplanar shielding structure further reduces parasitic capacitance fluctuations, significantly improving the accuracy and stability of the sensor; and generating an electromechanical coupling non-dominated solution set. In the above steps, accurate simulation and optimization reduce the negative impact of electromechanical coupling effects on circuit performance.

[0043] Furthermore, based on the initial circuit layout network and combined with the thermal isolation wiring constraints under the thermal feedback enhancement loop, the method of this application further includes:

[0044] Temperature field data, electric field data, and magnetic field data are mapped to the IC physical coordinate system. The coupling strength between adjacent sensors is set, and the coupling strength between adjacent sensors is used to quantify the spatial correlation between electrical crosstalk and magnetic interference. Based on the IC physical coordinate system and the coupling strength between adjacent sensors, mutual information nonlinear coupling analysis is performed with the temperature field and electric field coupling amplification partition, and thermal isolation wiring constraints under the thermal feedback enhancement loop are set.

[0045] Specifically, the IC (integrated circuit) physical coordinate system is a coordinate system based on the physical layout of integrated circuits. It is used to accurately describe the positions of various components and sensors on the integrated circuit substrate. Through the IC physical coordinate system, various physical field distribution analyses and coupling effects can be easily performed. Coupling strength refers to the strength of the interaction between two or more physical quantities. In integrated circuits, the coupling strength between adjacent sensors can quantify the spatial correlation of electrical crosstalk and magnetic interference. The quantification of coupling strength helps in designing wiring constraints to reduce these interferences. Mutual information is a statistical quantity that measures the interdependence between two random variables. In nonlinear coupling analysis, mutual information is used to analyze the complex interactions between different physical fields such as temperature field and electric field. By analyzing, regions with significant coupling effects, i.e., coupling amplification zones, can be identified. Thermal feedback reinforcement loops are used to monitor and regulate the temperature distribution in integrated circuits. Through thermal feedback reinforcement loops, the thermal management strategy of the circuit can be dynamically adjusted to reduce the impact of thermal expansion on circuit performance. Thermal isolation wiring constraints refer to wiring rules set for thermal management, which aim to reduce the propagation and accumulation of heat in the circuit. Specifically, measures such as setting thermal isolation slots and using low thermal conductivity materials can effectively reduce thermal coupling.

[0046] Execution steps: Map temperature field data, electric field data, and magnetic field data to the IC physical coordinate system. Through the IC physical coordinate system, accurately describe the position of each sensor and the physical field distribution around it; calculate the coupling strength between adjacent sensors and quantify the spatial correlation of electrical crosstalk and magnetic interference; based on the IC physical coordinate system and coupling strength data, perform mutual information nonlinear coupling analysis of the temperature field and electric field to identify regions with significant coupling effects, i.e., coupling amplification zones; based on these analysis results, set thermal isolation wiring constraints under the thermal feedback enhancement loop to reduce the impact of thermal coupling on circuit performance.

[0047] By employing mutual information nonlinear coupling analysis, regions exhibiting significant coupling effects between the temperature and electric fields are identified. By setting thermal isolation wiring constraints, the local temperature rise rate is reduced, thereby mitigating the impact of electrode spacing changes caused by thermal expansion on electric field stability. Specifically, mutual information nonlinear coupling analysis reduces coupling strength, thereby decreasing electrical crosstalk and magnetic interference. Furthermore, by setting thermal isolation wiring constraints, the local temperature rise rate is reduced, minimizing the impact of thermal expansion on electrode spacing, thus improving the performance and reliability of the integrated circuit under multi-physics interaction. In these steps, precise physical field analysis and coupling effect quantification optimize the integrated circuit wiring design, reducing the negative impact of thermal, electrical, and magnetic multi-physics coupling effects on circuit performance.

[0048] Furthermore, by performing mutual information nonlinear coupling analysis with the temperature field and electric field coupling amplification partition, and setting thermal isolation wiring constraints under the thermal feedback enhancement loop, the method of this application also includes:

[0049] Thermal amplification coupling analysis based on physical coupling effect under layout association is performed to construct the heat conduction path between sensor metal traces and adjacent functional units, determine the thermal resistance matrix, and predict the temperature rise gradient by combining dynamic power consumption waveform. When there is a coherent match between the local temperature rise rate and the main frequency of electric field fluctuation, the temperature field and electric field coupling amplification partition and the thermal feedback enhancement loop are determined.

[0050] Specifically, physical coupling effect refers to the phenomenon of interaction between different physical fields such as temperature field, electric field, and magnetic field. In integrated circuits, physical coupling effect affects circuit performance and stability. Layout association refers to the spatial relationship between different components and functional units in integrated circuit design. Layout association affects the distribution of physical fields and coupling effects. Thermal amplification coupling analysis refers to analyzing the interaction between the thermal field and other physical fields, especially in regions where changes in the thermal field cause significant changes in other physical fields. Sensor metal traces refer to the metal lines connecting sensors and other functional units. These lines transmit signals and conduct heat simultaneously. Thermal conduction path refers to the path of heat propagation in integrated circuits, usually determined by the thermal conductivity of the material and the layout of the components. The thermal resistance matrix is ​​used to describe the thermal resistance relationship between different components in an integrated circuit, determining the propagation and distribution of heat between different components.

[0051] Dynamic power consumption waveform refers to the power consumption of an integrated circuit over time during operation. By analyzing the dynamic power consumption waveform, the temperature rise gradient, i.e., the rate of temperature change over time, can be predicted. Coherence matching refers to the significant correlation between the local temperature rise rate and the dominant frequency of electric field fluctuations. When the frequencies of these two signals are matched, the interaction between the local temperature rise rate and the dominant frequency of electric field fluctuations will be significantly enhanced. Temperature field and electric field coupling amplification zone refers to the region in the integrated circuit where the coupling effect between the temperature field and the electric field is significantly enhanced. In the temperature field and electric field coupling amplification zone, changes in the thermal field will lead to significant changes in the electric field, and vice versa. Thermal feedback enhancement loop is used to monitor and regulate the temperature distribution in the integrated circuit. Through the thermal feedback enhancement loop, the thermal management strategy of the circuit can be dynamically adjusted to reduce the impact of thermal expansion on circuit performance.

[0052] Execution Steps: Based on physical coupling effects, thermal amplification coupling analysis is performed under layout correlation. Specifically, using finite element simulation and other methods, the heat conduction path between the sensor metal traces and adjacent functional units is constructed; the thermal resistance matrix is ​​determined, and the propagation and distribution of heat between different components are calculated using the thermal resistance matrix; combined with dynamic power consumption waveforms, the temperature rise gradient, i.e., the rate of temperature change over time, is predicted; when there is a coherent match between the local temperature rise rate and the dominant frequency of electric field fluctuations, the temperature field and electric field coupling amplification zone is determined, and a thermal feedback reinforcement loop is set to dynamically adjust the temperature distribution and reduce the impact of thermal expansion on circuit performance. Through thermal amplification coupling analysis, the coupling strength of the temperature field and electric field coupling amplification zone is reduced, thus reducing electric field fluctuations; by setting up a thermal feedback reinforcement loop, the local temperature rise rate is reduced, thus reducing the impact of thermal expansion on electrode spacing. In the above steps, through accurate thermal amplification coupling analysis, regions with significant temperature field and electric field coupling effects in the integrated circuit are identified and optimized, reducing the negative impact of thermal expansion on circuit performance.

[0053] Furthermore, based on the initial circuit layout network and considering the electromagnetic isolation wiring constraints under the electromagnetic resonant circuit, the method of this application further includes:

[0054] Based on the IC physical coordinate system and the coupling strength between adjacent sensors, a mutual information nonlinear coupling analysis is performed on the magnetic field and electric field coupling amplification partition, and electromagnetic isolation wiring constraints are set under the electromagnetic resonant circuit.

[0055] Specifically, the IC physical coordinate system is a coordinate system based on the physical layout of integrated circuits. It is used to accurately describe the position of each component and sensor on the IC substrate. Through the IC physical coordinate system, it is convenient to perform distribution analysis of various physical fields and quantify coupling effects. Coupling strength refers to the strength of the interaction between two or more physical quantities. In integrated circuits, the coupling strength between adjacent sensors can quantify the spatial correlation of electric crosstalk corresponding to mutual electric field interference and magnetic interference corresponding to mutual magnetic field interference. The quantification of coupling strength helps to design wiring constraints to reduce these interferences.

[0056] In integrated circuits, the region where magnetic and electric field coupling amplification is significantly enhanced refers to the area where the coupling effect between magnetic and electric fields is significantly amplified. In this region, changes in the magnetic field lead to significant changes in the electric field, and vice versa. Special routing and design strategies are typically required to reduce coupling effects. In nonlinear coupling analysis, mutual information is used to analyze the complex interactions between different physical fields, identifying regions with significant coupling effects, i.e., coupling amplification regions. Electromagnetic resonant circuits are circuit structures used to monitor and regulate the distribution of electromagnetic fields. By using electromagnetic resonant circuits, the distribution of electromagnetic fields is optimized, reducing electromagnetic interference. Electromagnetic isolation routing constraints refer to routing rules set for electromagnetic compatibility, aiming to reduce mutual interference between electromagnetic fields. Specifically, measures such as setting shielding layers and optimizing routing paths can effectively reduce electromagnetic coupling.

[0057] Execution Steps: Based on the coupling strength data between the IC physical coordinate system and adjacent sensors, perform mutual information nonlinear coupling analysis on the magnetic and electric field coupling amplification zones; identify regions with significant magnetic and electric field coupling effects, i.e., coupling amplification zones, using finite element simulation and other methods; based on the analysis results, set electromagnetic isolation wiring constraints under the electromagnetic resonant circuit to reduce the negative impact of electromagnetic coupling on circuit performance. Specific measures include, but are not limited to, optimizing wiring paths, setting electromagnetic shielding layers, and adjusting component layout. Through mutual information nonlinear coupling analysis, regions with significant magnetic and electric field coupling effects are identified, reducing the coupling strength of the magnetic and electric field coupling amplification zones and reducing electromagnetic interference; by setting electromagnetic isolation wiring constraints, electromagnetic interference is reduced, significantly improving the accuracy and stability of the sensors. In the above steps, through precise physical field analysis and coupling effect quantification, the wiring design of the integrated circuit is optimized, reducing the negative impact of electromagnetic coupling effects on circuit performance.

[0058] Furthermore, the method of this application includes:

[0059] Electromagnetic amplification coupling analysis based on physical coupling effect is performed under layout correlation. The electromagnetic conduction path between sensor metal traces and adjacent functional units is constructed, the mutual inductance matrix is ​​determined, and the magnetic field strength gradient is predicted by combining dynamic power consumption waveform. When the local magnetic field change rate and the electric field fluctuation main frequency have coherent matching, the magnetic field and electric field coupling amplification partition and the electromagnetic resonant circuit are determined.

[0060] Specifically, physical coupling effect refers to the phenomenon of interaction between different physical fields. In integrated circuits, physical coupling effect affects the performance and stability of the circuit. Layout association refers to the spatial relationship between different components and functional units in integrated circuit design. This relationship affects the distribution of physical fields and coupling effects. Electromagnetic amplification coupling analysis refers to analyzing the interaction between electromagnetic fields, especially in regions where changes in electromagnetic fields cause significant changes in other physical fields. Sensor metal traces refer to the metal lines connecting sensors and other functional units. These lines transmit signals and conduct electromagnetic energy simultaneously. Electromagnetic conduction path refers to the path of electromagnetic energy propagation in integrated circuits, which is usually determined by the electromagnetic properties of materials, including permeability and dielectric constant, and the layout of components. Mutual inductance matrix is ​​used to describe the electromagnetic coupling relationship between different components in integrated circuits. Through the mutual inductance matrix, the propagation and distribution of electromagnetic energy between different components can be analyzed.

[0061] Dynamic power consumption waveform refers to the power consumption of an integrated circuit over time during operation. By analyzing the dynamic power consumption waveform, the magnetic field strength gradient, i.e., the rate of change of the magnetic field strength over time, can be predicted. Coherence matching refers to the significant correlation between the local magnetic field change rate and the dominant frequency of the electric field fluctuation. When the frequencies of these two signals are matched, the interaction between the local magnetic field change rate and the dominant frequency of the electric field fluctuation will be significantly enhanced. Magnetic field and electric field coupling amplification zone refers to the region in an integrated circuit where the coupling effect between the magnetic field and the electric field is significantly enhanced. In the magnetic field and electric field coupling amplification zone, changes in the magnetic field will lead to significant changes in the electric field, and vice versa. Electromagnetic resonant circuit is a circuit structure used to monitor and adjust the distribution of electromagnetic fields. Through electromagnetic resonant circuits, the distribution of electromagnetic fields can be optimized and electromagnetic interference reduced.

[0062] Execution Steps: Electromagnetic amplification coupling analysis based on physical coupling effects is performed under layout correlation. Specifically, using finite element simulation and other methods, the electromagnetic conduction path between the sensor's metal traces and adjacent functional units is constructed. The mutual inductance matrix is ​​determined, and the propagation and distribution of electromagnetic energy among different components are calculated using this matrix. Combined with dynamic power consumption waveforms, the magnetic field strength gradient, i.e., the rate of change of magnetic field strength over time, is predicted. When there is a coherent match between the local magnetic field change rate and the dominant frequency of electric field fluctuations, the magnetic field and electric field coupling amplification zones are identified, and electromagnetic resonant circuits are set up to dynamically adjust the electromagnetic field distribution and reduce electromagnetic interference. Through electromagnetic amplification coupling analysis, the coupling strength of the magnetic field and electric field coupling amplification zones is reduced, thus reducing electromagnetic interference. By setting up electromagnetic resonant circuits, the local magnetic field change rate is reduced, thereby reducing electric field fluctuations caused by electromagnetic coupling. In the above steps, through precise electromagnetic amplification coupling analysis, regions with significant magnetic field and electric field coupling effects in integrated circuits are identified and optimized, reducing the negative impact of electromagnetic coupling on circuit performance.

[0063] Furthermore, the method of this application includes:

[0064] On the integrated circuit substrate, a physical sensor array covering the IC core area is used to collect temperature field data, electric field data, and magnetic field data of the integrated circuit substrate during simulated operation. Based on the temperature field data, electric field data, and magnetic field data, the dominant frequency of temperature fluctuation, electric field coupling strength, and magnetic field coupling strength are extracted, and inter-field correlation feature analysis is performed to identify the physical coupling effect that affects the layout performance.

[0065] Specifically, the integrated circuit substrate is the physical support structure of the integrated circuit, used to fix and connect various electronic components, such as sensors and transistors. It is usually made of insulating materials and has good thermal conductivity and electrical insulation. The physical sensor array is a network of multiple sensors used to monitor and collect data on the physical field. In integrated circuit design, the multiple sensors corresponding to the physical sensor array usually cover the core area of ​​the IC to monitor changes in the physical field during operation in real time. The simulated operation process refers to simulating the operating state of the integrated circuit under actual working conditions through simulation software or actual testing equipment, including applying working voltage, input signals, etc., to observe the circuit's response and performance.

[0066] Temperature fluctuation frequency refers to the main frequency component of temperature change. Furthermore, the temperature fluctuation frequency can reflect the dynamic characteristics of a heat source, such as the temperature rise frequency caused by changes in power consumption. Electric field coupling strength refers to the strength of interaction between different electric fields. Furthermore, electric field coupling strength can quantify the mutual interference between electric fields, such as electric crosstalk. Magnetic field coupling strength refers to the strength of interaction between different magnetic fields. Furthermore, magnetic field coupling strength can quantify the mutual interference between magnetic fields, such as magnetic interference. Inter-field correlation characteristic analysis refers to analyzing the interrelationships and coupling effects between different physical fields. By analyzing and identifying the correlation characteristics between physical fields, circuit layout can be optimized. Physical coupling effect refers to the phenomenon of interaction between different physical fields. Furthermore, physical coupling effect can affect the performance and stability of a circuit.

[0067] Execution steps: A physical sensor array covering the IC core area is used on the integrated circuit substrate to collect temperature, electric, and magnetic field data during simulated operation. Multiple sensors corresponding to the physical sensor array monitor the physical field changes of the circuit under different operating conditions in real time. Based on the collected temperature, electric, and magnetic field data, the dominant frequency of temperature fluctuations, electric field coupling strength, and magnetic field coupling strength are extracted. Through inter-field correlation feature analysis, physical coupling effects affecting layout performance are identified. Furthermore, the coherence between the dominant frequency of temperature fluctuations and the dominant frequency of electric field fluctuations, as well as the spatial distribution of electric and magnetic field coupling strengths, are analyzed to determine regions with significant coupling effects. The data collected by the physical sensor array improves the accuracy of temperature fluctuation frequency, electric field coupling strength, and magnetic field coupling strength measurement. Inter-field correlation feature analysis improves the accuracy of identifying regions with significant coupling effects. In the above steps, through precise physical field monitoring and analysis, regions with significant physical coupling effects in the integrated circuit are identified and optimized, reducing the negative impact of physical field coupling on circuit performance.

[0068] In summary, the beneficial effects of the embodiments of this application are:

[0069] This application utilizes an integrated circuit substrate-based approach and apparatus to optimize integrated circuit layout. An initial circuit layout network is established. Based on this network, and considering the thermal isolation wiring constraints under the thermal feedback reinforcement loop and the electromagnetic isolation wiring constraints under the electromagnetic resonant loop, candidate circuit layout combinations are determined. These combinations are then input into a multi-objective optimization engine for Pareto front search, yielding non-dominated solutions for thermoelectric coupling, magnetomechanical coupling, and electromechanical coupling, ultimately determining the globally optimal circuit layout combination. This approach combines the thermal isolation constraints of the thermal feedback reinforcement loop with the electromagnetic isolation constraints of the electromagnetic resonant loop, introduces a multi-objective optimization engine for Pareto front search, and simultaneously generates non-dominated solutions for thermoelectric coupling, magnetomechanical coupling, and electromechanical coupling. This effectively controls the adverse effects of multi-physics interactions on circuit performance and improves the operational stability of sensor-based integrated circuits.

[0070] Example 2, based on the same inventive concept as the sensor-based integrated circuit layout optimization method in the foregoing examples, such as... Figure 2 As shown in the figure, this application provides a sensor-based integrated circuit layout optimization device, wherein the device includes:

[0071] Initial circuit layout network setting module M100: Based on the integrated circuit substrate, it sets the initial circuit layout network.

[0072] Candidate circuit layout combination determination module M200: Based on the initial circuit layout network, and combined with the thermal isolation wiring constraints under the thermal feedback enhancement loop and the electromagnetic isolation wiring constraints under the electromagnetic resonant loop, the candidate circuit layout combination is determined.

[0073] Search module M300: Inputs the candidate circuit layout combination into the multi-objective optimization engine to perform Pareto front search, obtains the non-dominated solution sets of thermoelectric coupling, magnetomechanical coupling, and electromechanical coupling, and determines the globally optimal circuit layout combination.

[0074] Furthermore, the search module M300 is used to perform the following method:

[0075] The influence of temperature distribution on the electric field is simulated by finite element method. The effects of temperature rise on carrier mobility change and leakage current increase are analyzed. The electrode spacing change caused by thermal expansion is dynamically optimized. Thermal through-hole array and thermal conductivity isolation groove are introduced to determine the non-dominated solution set of thermoelectric coupling.

[0076] Furthermore, the search module M300 is also used to perform the following methods:

[0077] For magnetically sensitive components including TMR and Hall elements, the mechanical deformation caused by magnetostriction is analyzed, the orientation and layout position on the integrated circuit substrate are optimized, and the non-dominated solution set of magnetomechanical coupling is determined.

[0078] Furthermore, the search module M300 is also used to perform the following methods:

[0079] For electrical sensitive components including electrostatic sensors and MEMS capacitive sensing units, the electrode spacing and support structure stiffness are adjusted to determine the electromechanical coupling non-dominated solution set; at the same time, a coplanar shielding structure is introduced in the wiring path to suppress parasitic capacitance fluctuations induced by mechanical deformation.

[0080] Furthermore, the candidate circuit layout combination determination module M200 is also used to perform the following method:

[0081] Temperature field data, electric field data, and magnetic field data are mapped to the IC physical coordinate system. The coupling strength between adjacent sensors is set, and the coupling strength between adjacent sensors is used to quantify the spatial correlation between electrical crosstalk and magnetic interference. Based on the IC physical coordinate system and the coupling strength between adjacent sensors, mutual information nonlinear coupling analysis is performed with the temperature field and electric field coupling amplification partition, and thermal isolation wiring constraints under the thermal feedback enhancement loop are set.

[0082] Furthermore, the candidate circuit layout combination determination module M200 is also used to perform the following method:

[0083] Thermal amplification coupling analysis based on physical coupling effect under layout association is performed to construct the heat conduction path between sensor metal traces and adjacent functional units, determine the thermal resistance matrix, and predict the temperature rise gradient by combining dynamic power consumption waveform. When there is a coherent match between the local temperature rise rate and the main frequency of electric field fluctuation, the temperature field and electric field coupling amplification partition and the thermal feedback enhancement loop are determined.

[0084] Furthermore, the candidate circuit layout combination determination module M200 is also used to perform the following method:

[0085] Based on the IC physical coordinate system and the coupling strength between adjacent sensors, a mutual information nonlinear coupling analysis is performed on the magnetic field and electric field coupling amplification partition, and electromagnetic isolation wiring constraints are set under the electromagnetic resonant circuit.

[0086] Furthermore, the candidate circuit layout combination determination module M200 is also used to perform the following method:

[0087] Electromagnetic amplification coupling analysis based on physical coupling effect is performed under layout correlation. The electromagnetic conduction path between sensor metal traces and adjacent functional units is constructed, the mutual inductance matrix is ​​determined, and the magnetic field strength gradient is predicted by combining dynamic power consumption waveform. When the local magnetic field change rate and the electric field fluctuation main frequency have coherent matching, the magnetic field and electric field coupling amplification partition and the electromagnetic resonant circuit are determined.

[0088] Furthermore, the candidate circuit layout combination determination module M200 is also used to perform the following method:

[0089] On the integrated circuit substrate, a physical sensor array covering the IC core area is used to collect temperature field data, electric field data, and magnetic field data of the integrated circuit substrate during simulated operation. Based on the temperature field data, electric field data, and magnetic field data, the dominant frequency of temperature fluctuation, electric field coupling strength, and magnetic field coupling strength are extracted, and inter-field correlation feature analysis is performed to identify the physical coupling effect that affects the layout performance.

[0090] In summary, any step can be stored as a computer instruction or program in an unrestricted computer memory and can be called and recognized by an unrestricted computer processor; no further restrictions are imposed here.

[0091] Furthermore, the above technical solutions only embody the preferred technical solutions of the embodiments of this application. Any changes that those skilled in the art may make to certain parts of these solutions embody the novel principles of the embodiments of this application. Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of this application.

Claims

1. A method for optimizing placement of integrated circuits based on sensors, the method comprising: The method comprises: Based on the integrated circuit substrate, an initial circuit arrangement network is set; Based on the initial circuit arrangement network, combined with the thermal isolation wiring constraint condition under the thermal feedback reinforcement loop and the electromagnetic isolation wiring constraint condition under the electromagnetic resonance loop, a candidate circuit arrangement combination is determined; The candidate circuit arrangement combination is input into a multi-objective optimization engine for Pareto front search to obtain a set of thermal-electric coupling non-dominated solutions, a set of magnetic-mechanical coupling non-dominated solutions, and a set of electric-mechanical coupling non-dominated solutions, and a globally optimal circuit arrangement combination is determined; To obtain the set of thermal-electric coupling non-dominated solutions, the method comprises: Through finite element simulation to simulate the influence of temperature distribution on electric field, to analyze the carrier mobility change and leakage current increase effect caused by temperature rise, to dynamically optimize the electrode spacing change caused by thermal expansion, and to introduce a thermal via array and a thermal conductivity isolation groove, the set of thermal-electric coupling non-dominated solutions is determined; To obtain the set of magnetic-mechanical coupling non-dominated solutions, the method comprises: For the magnetic sensitive components including TMR and Hall element, the mechanical deformation caused by magnetostriction effect is analyzed, the orientation and layout position on the integrated circuit substrate are optimized, and the set of magnetic-mechanical coupling non-dominated solutions is determined; To obtain the set of electric-mechanical coupling non-dominated solutions, the method comprises: For the electric sensitive components including electrostatic sensor and MEMS capacitive sensing unit, the electrode spacing and support structure stiffness are adjusted, and the set of electric-mechanical coupling non-dominated solutions is determined; At the same time, a coplanar shielding structure is introduced in the wiring path, and the coplanar shielding structure is used to suppress the parasitic capacitance fluctuation induced by mechanical deformation; Based on the initial circuit arrangement network, combined with the thermal isolation wiring constraint condition under the thermal feedback reinforcement loop, the method further comprises: Temperature field data, electric field data and magnetic field data are mapped to an IC physical coordinate system, and the coupling strength between adjacent sensors is set, which is used to quantify the spatial correlation of electric crosstalk and magnetic interference; Based on the IC physical coordinate system and the coupling strength between adjacent sensors, mutual information nonlinear coupling analysis is performed on the temperature field and electric field coupling amplification partition, and the thermal isolation wiring constraint condition under the thermal feedback reinforcement loop is set; The mutual information nonlinear coupling analysis is performed on the temperature field and electric field coupling amplification partition, and the thermal isolation wiring constraint condition under the thermal feedback reinforcement loop is set, the method comprising: Based on the physical coupling effect, thermal amplification coupling analysis is performed under layout correlation, a thermal conduction path between sensor metal traces and adjacent functional units is constructed, a thermal resistance matrix is determined, and a temperature rise gradient is predicted in combination with a dynamic power consumption waveform, when the local temperature rise rate and the electric field fluctuation main frequency exist coherence matching, the temperature field and electric field coupling amplification partition and the thermal feedback reinforcement loop are determined; The method comprises: Based on the physical coupling effect, electromagnetic amplification coupling analysis is performed under layout correlation, an electromagnetic conduction path between sensor metal traces and adjacent functional units is constructed, a mutual inductance matrix is determined, and a magnetic field intensity gradient is predicted in combination with a dynamic power consumption waveform, when the local magnetic field change rate and the electric field fluctuation main frequency exist coherence matching, the magnetic field and electric field coupling amplification partition and the electromagnetic resonance loop are determined.

2. The sensor-based integrated circuit placement optimization method of claim 1, wherein, Based on the initial circuit arrangement network, combined with the electromagnetic isolation wiring constraint condition under the electromagnetic resonance loop, the method further comprises: Based on the IC physical coordinate system, the coupling strength between adjacent sensors, and the magnetic field and electric field coupling amplification partition, mutual information nonlinear coupling analysis is performed, and the electromagnetic isolation wiring constraint condition under the electromagnetic resonance loop is set.

3. The sensor-based integrated circuit placement optimization method of claim 1, wherein, The method comprises: On the integrated circuit substrate, a physical sensor array covering the IC core area is used to collect temperature field data, electric field data, and magnetic field data of the integrated circuit substrate during simulation operation; According to the temperature field data, electric field data, and magnetic field data, the temperature fluctuation main frequency, electric field coupling strength, and magnetic field coupling strength are extracted, and the correlation characteristics between fields are analyzed to identify the physical coupling effect affecting the layout performance.

4. A sensor-based integrated circuit placement optimization apparatus, characterized by The device for implementing the steps of the sensor-based integrated circuit arrangement optimization method of any one of claims 1-3 comprises: An initial circuit arrangement network setting module: based on an integrated circuit substrate, an initial circuit arrangement network is set; A candidate circuit arrangement combination determination module: based on the initial circuit arrangement network, combined with the thermal isolation wiring constraint condition under the thermal feedback reinforcement loop and the electromagnetic isolation wiring constraint condition under the electromagnetic resonance loop, a candidate circuit arrangement combination is determined; A search module: the candidate circuit arrangement combination is input into a multi-objective optimization engine for Pareto front search to obtain thermal-electric coupling non-dominated solution set, magnetic-mechanical coupling non-dominated solution set, and electric-mechanical coupling non-dominated solution set, and determine the global optimal circuit arrangement combination.

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