3D logic circuit virtual simulation method and system

Abstract

The invention relates to the technical field of virtual simulation, and discloses a 3D logic circuit virtual simulation method and system, and the method comprises the steps: carrying an intelligent monitoring system, monitoring the operation data of manufacturing equipment, and integrating the operation data of the equipment; obtaining material performance data based on a manufacturer system database, and integrating the obtained material performance data; establishing an environment monitoring system to obtain environment monitoring parameters, and performing data integration on the environment monitoring parameters; based on an artificial intelligence algorithm, fusing the equipment operation data, the material performance data and the environment monitoring parameters to calculate a simulation error coefficient; and triggering a simulation compensation mechanism based on the simulation error coefficient. According to the method, the quality problem caused by unconsidered deviation in traditional manufacturing is avoided, the cost of repeated trial and error of a physical prototype is reduced, meanwhile, it is ensured that the geometric accuracy of a final finished product is consistent with a design target through accurate rehearsal of a simulation level, and the stability and reliability of 3D logic circuit manufacturing are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of virtual simulation technology, specifically to a 3D logic circuit virtual simulation method and system. Background Technology

[0002] 3D logic circuit virtual simulation is a digital design and verification system that integrates 3D graphics, semiconductor physical modeling, and real-time computing technologies. Essentially, it constructs a virtual silicon-based space with physical properties through mathematical models, enabling visualization and interaction of microstructures such as CMOS transistors, interconnect metal layers, and package pins on a macroscopic scale. Compared to traditional SPICE simulation or FPGA prototyping, this system boasts three major breakthroughs: expanded spatial dimensions (from a two-dimensional plane to a three-dimensional stack), compressed temporal dimensions (reducing nanosecond-level physical processes to millisecond-level rendering), and enhanced sensory dimensions (visual / auditory / tactile multimodal feedback). This technological paradigm shift allows engineers to directly "enter" the chip's interior to observe abstract physical phenomena such as carrier migration and electric field distribution. 3D logic circuit virtual simulation is a digital circuit design and verification method based on 3D visualization technology. It constructs three-dimensional circuit models using computer software, enabling learners or engineers to build, debug, and test logic circuits in a virtual environment. Compared to traditional two-dimensional circuit diagrams, 3D simulation can more intuitively display the spatial layout of circuit components, signal flow, and logical relationships, supporting interactive operations (such as toggle switches and signal injection) and real-time result observation (such as LED on / off states and waveform changes). This technology is widely used in electronic education, chip prototyping, and complex system design, significantly reducing hardware costs and improving development efficiency.

[0003] In the manufacturing process of 3D logic circuits, equipment, material performance, and environment together constitute the root cause of deviations. They are like an intercoupled error triangle, systematically affecting the geometric accuracy of the final product. The existing manufacturing process of 3D logic circuits does not fully consider the deviations caused by equipment, material performance, and environment, which increases development costs, makes it impossible to guarantee the consistency and yield of mass production, and seriously restricts the industrial application of the technology. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a 3D logic circuit virtual simulation method and system, which avoids quality problems caused by unconsidered deviations in traditional manufacturing, reduces the cost of repeated trial and error of physical prototypes, and ensures that the geometric accuracy of the final product is consistent with the design target through accurate pre-simulation at the simulation level, thus significantly improving the stability and reliability of 3D logic circuit manufacturing.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution: a 3D logic circuit virtual simulation method, comprising the following steps:

[0008] Step 1: Install an intelligent monitoring system to monitor the operating data of the manufacturing equipment and integrate the equipment operating data;

[0009] Step 2: Obtain material performance data from the manufacturer's system database and integrate the obtained material performance data;

[0010] Step 3: Establish an environmental monitoring system to acquire environmental monitoring parameters and integrate the environmental monitoring parameters;

[0011] Step 4: Calculate the simulation error coefficient based on artificial intelligence algorithms, integrating equipment operation data, material performance data, and environmental monitoring parameters;

[0012] Step 5: Trigger the simulation compensation mechanism based on the simulation error coefficient.

[0013] Preferably, the equipment operating data includes printhead position, extruder actual speed, substrate processing dimensional deviation, 3D stacking assembly deviation, and pick-and-place machine positioning repeatability. The integrated numbering expression for the equipment operating data is as follows:

[0014] SYsj=[Sdw, Sjz, Sjc, Spp, Stj

[0015] In the expression, SYsj represents the equipment operation data, Sdw represents the printhead position in the equipment operation data, Sjz represents the actual speed of the extruder in the equipment operation data, Sjc represents the substrate processing size deviation in the equipment operation data, Spp represents the 3D stacking assembly deviation in the equipment operation data, and Stj represents the positioning repeatability accuracy of the pick-and-place machine in the equipment operation data.

[0016] Preferably, the material performance data includes linear shrinkage rate, coefficient of thermal expansion, and molding compound curing shrinkage rate, and the integrated numbering expression for the material performance data is:

[0017] CXsj = [Cxs, Crp, Cgs]

[0018] In the expression, CXsj represents the material performance data, Cxs represents the linear shrinkage rate in the material performance data, Crp represents the coefficient of thermal expansion in the material performance data, and Cgs represents the curing shrinkage rate of the molding compound in the material performance data.

[0019] Preferably, the environmental monitoring parameters include: ambient temperature, ambient relative humidity, standard ambient temperature, and standard ambient relative humidity, and the integrated numbering expression for the environmental monitoring parameters is:

[0020] HJsj=[Hwd, Hsd, Hbw, Hbs]

[0021] In the expression, HJsj represents the environmental monitoring parameter, Hwd represents the ambient temperature among the environmental monitoring parameters, Hsd represents the ambient relative humidity among the environmental monitoring parameters, Hbw represents the standard ambient temperature among the environmental monitoring parameters, and Hbs represents the standard ambient relative humidity among the environmental monitoring parameters.

[0022] Preferably, the formula for calculating the simulation error coefficient is:

[0023] W = μ·Sy + θ·Cy + ρ·Hy

[0024] In the formula, W represents the simulation error coefficient, Sy represents the equipment operation influence coefficient, μ represents the weight of the equipment operation influence coefficient, Cy represents the material performance influence coefficient, θ represents the weight of the material performance influence coefficient, Hy represents the environmental interference index, and ρ represents the weight of the environmental interference index.

[0025] Preferably, the formula for calculating the equipment operation impact coefficient is as follows:

[0026]

[0027] In the formula, Sy represents the equipment operation influence coefficient, Si represents the i-th parameter in the equipment operation data, Zi represents the preset parameter of the i-th parameter in the equipment operation data, Ci represents the allowable difference of the i-th parameter in the equipment operation data, and ai represents the weight of the i-th parameter in the equipment operation data.

[0028] Preferably, the formula for calculating the material property influence coefficient is as follows:

[0029]

[0030] In the formula, Cy represents the material performance influence coefficient, Yj represents the j-th parameter in the material performance data, Fj represents the preset parameter of the j-th parameter in the material performance data, Cj represents the allowable difference of the j-th parameter in the material performance data, and βi represents the weight of the j-th parameter in the material performance data.

[0031] Preferably, the formula for calculating the environmental disturbance index is:

[0032]

[0033] In the formula, Hy represents the environmental disturbance index, M represents the allowable difference of environmental temperature, N represents the allowable difference of environmental relative humidity, δ1 represents the weight of environmental temperature, and (1-δ1) represents the weight of environmental relative humidity.

[0034] Preferably, the simulation compensation mechanism is triggered when the simulation error coefficient exceeds 0.5.

[0035] A 3D logic circuit virtual simulation system, applied to 3D logic circuit virtual simulation methods, includes an intelligent monitoring module, a data integration module, an intelligent algorithm module, and a simulation compensation module;

[0036] The intelligent monitoring module is used to integrate the intelligent monitoring system, connect to the manufacturer's system database, and establish an environmental monitoring system.

[0037] The data integration module acquires equipment operation data, material performance data, and environmental monitoring parameters based on the intelligent monitoring module.

[0038] The intelligent algorithm module is used to integrate artificial intelligence algorithms to calculate simulation error coefficients;

[0039] The simulation compensation module implements a simulation compensation mechanism based on the simulation error coefficient.

[0040] Compared with the prior art, the present invention provides a 3D logic circuit virtual simulation method and system, which has the following beneficial effects:

[0041] This invention comprehensively captures real data on equipment operation, material performance, and environmental parameters through an intelligent monitoring system, manufacturer database, and environmental monitoring system. This avoids information loss caused by ignoring some deviation sources in traditional manufacturing. With the help of artificial intelligence algorithms and standardized coefficient formulas, the coupling deviation of the three factors is transformed into quantifiable simulation error coefficients, realizing the transformation from fuzzy influence to precise calculation. Finally, by using a compensation trigger threshold, compensation is initiated in time when the error coefficient exceeds the limit. This can correct geometric deviations caused by insufficient equipment processing precision, material deformation, and environmental fluctuations in advance during the virtual simulation stage. This not only avoids quality problems caused by neglecting deviations in traditional manufacturing, but also reduces the cost of repeated trial and error of physical prototypes. At the same time, through precise pre-simulation at the simulation level, it ensures that the geometric accuracy of the final product is consistent with the design target, significantly improving the stability and reliability of 3D logic circuit manufacturing. Attached Figure Description

[0042] Figure 1 This is a diagram illustrating the steps of the method of the present invention;

[0043] Figure 2 This is a schematic diagram of the system flow of the present invention. Detailed Implementation

[0044] 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, and 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.

[0045] Please see Figures 1-2 A 3D logic circuit virtual simulation method includes the following steps:

[0046] Step 1: Install an intelligent monitoring system to monitor the operating data of the manufacturing equipment and integrate the equipment operating data;

[0047] Equipment operation data includes printhead position, extruder actual speed, substrate processing dimensional deviation, 3D stacking assembly deviation, and pick-and-place machine positioning repeatability. The integrated number expression for equipment operation data is:

[0048] SYsj=[Sdw, Sjz, Sjc, Spp, Stj]

[0049] In the expression, SYsj represents the equipment operation data, Sdw represents the printhead position in the equipment operation data, Sjz represents the actual speed of the extruder in the equipment operation data, Sjc represents the substrate processing size deviation in the equipment operation data, Spp represents the 3D stacking assembly deviation in the equipment operation data, and Stj represents the positioning repeatability accuracy of the pick-and-place machine in the equipment operation data.

[0050] Step 1 achieves comprehensive perception and dynamic control of key process parameters by equipping an intelligent monitoring system. It quantifies the status of equipment scattered in various links into a traceable digital twin image, breaks down information silos, provides accurate basis for process optimization of 3D logic circuit virtual simulation, provides early warning of potential faults to avoid batch defects, reflects the fluctuation characteristics of the real production line, and provides support for realizing a production mode that shifts from experience-driven to data-intelligent decision-making.

[0051] Step 2: Obtain material performance data from the manufacturer's system database and integrate the obtained material performance data;

[0052] Material performance data includes linear shrinkage rate, coefficient of thermal expansion, and molding compound curing shrinkage rate. The integrated numbering expression for material performance data is as follows:

[0053] CXsj = [Cxs, Crp, Cgs]

[0054] In the expression, CXsj represents the material performance data, Cxs represents the linear shrinkage rate in the material performance data, Crp represents the coefficient of thermal expansion in the material performance data, and Cgs represents the curing shrinkage rate of the molding compound in the material performance data.

[0055] Step two involves standardizing and integrating material performance data by connecting to the manufacturer's system database, forming a traceable performance archive. This transforms the originally discrete material properties into calculable digital assets, providing accurate data input conditions for subsequent error simulation and supporting the improvement of the consistency between virtual verification and actual molding.

[0056] Step 3: Establish an environmental monitoring system to acquire environmental monitoring parameters and integrate the environmental monitoring parameters;

[0057] Environmental monitoring parameters include: ambient temperature, ambient relative humidity, standard ambient temperature, and standard ambient relative humidity. The integrated numbering expression for environmental monitoring parameters is:

[0058] HJsj=[Hwd, Hsd, Hbw, Hbs]

[0059] In the expression, HJsj represents the environmental monitoring parameter, Hwd represents the ambient temperature among the environmental monitoring parameters, Hsd represents the ambient relative humidity among the environmental monitoring parameters, Hbw represents the standard ambient temperature among the environmental monitoring parameters, and Hbs represents the standard ambient relative humidity among the environmental monitoring parameters.

[0060] Step 3 involves deploying an environmental monitoring system to achieve panoramic perception and quantitative control of production conditions, providing real-time updated environmental reference factors, breaking the cognitive limitations of traditional single-factor attribution, and realizing resonance that fully considers environmental conditions, providing environmental data as a reference for subsequent multi-source error fusion analysis;

[0061] Step 4: Calculate the simulation error coefficient based on artificial intelligence algorithms, integrating equipment operation data, material performance data, and environmental monitoring parameters;

[0062] The formula for calculating the simulation error coefficient is:

[0063] W = μ·Sy + θ·Cy + ρ·Hy

[0064] In the formula, W represents the simulation error coefficient, Sy represents the equipment operation influence coefficient, μ represents the weight of the equipment operation influence coefficient, Cy represents the material performance influence coefficient, θ represents the weight of the material performance influence coefficient, Hy represents the environmental interference index, and ρ represents the weight of the environmental interference index.

[0065] The calculation formula for the simulation error coefficient incorporates the equipment operation influence coefficient, material performance influence coefficient, and environmental interference index. It fully considers equipment, material performance, and environment to construct a multi-dimensional error assessment model. By combining weighting factors, it achieves error traceability and quantitative control over the entire manufacturing process of 3D logic circuits. This breaks through the traditional post-inspection mode, pre-calculates deformation trends under multi-factor coupling in virtual space, guides front-end design to avoid error risks, and realizes the leap from experience-driven to data intelligence, providing a deterministic mathematical anchor for precision manufacturing.

[0066] The formula for calculating the equipment operation impact coefficient is:

[0067]

[0068] In the formula, Sy represents the equipment operation impact coefficient, Si represents the i-th parameter in the equipment operation data, Zi represents the preset parameter of the i-th parameter in the equipment operation data, Ci represents the allowable difference of the i-th parameter in the equipment operation data, and ai represents the weight of the i-th parameter in the equipment operation data.

[0069] The formula for calculating the equipment operation impact coefficient integrates actual parameters, preset parameters, allowable differences, and weights in the equipment operation data. It can quantify the equipment operation status with specific values, fully reflect the impact of the equipment on deviations, and avoid the ambiguity of relying solely on subjective judgment or single data, making the evaluation results more comprehensive and objective.

[0070] The formula for calculating the influence coefficient of material properties is:

[0071]

[0072] In the formula, Cy represents the material performance influence coefficient, Yj represents the j-th parameter in the material performance data, Fj represents the preset parameter of the j-th parameter in the material performance data, Cj represents the allowable difference of the j-th parameter in the material performance data, and βi represents the weight of the j-th parameter in the material performance data.

[0073] By simultaneously considering multiple parameters related to material properties, the overall impact on material properties is comprehensively evaluated, avoiding the bias caused by single data. The weighting coefficients more accurately reflect the actual situation of material properties and fully consider the impact of material properties on deviations.

[0074] The formula for calculating the environmental disturbance index is:

[0075]

[0076] In the formula, Hy represents the environmental disturbance index, M represents the allowable difference of environmental temperature, N represents the allowable difference of environmental relative humidity, δ1 represents the weight of environmental temperature, and (1-δ1) represents the weight of environmental relative humidity.

[0077] The formula for calculating the environmental disturbance index takes into account two key factors: ambient temperature and relative humidity. Compared with methods that judge the degree of environmental disturbance based on only a single factor, it can more comprehensively and objectively reflect the impact of actual environmental conditions on the error.

[0078] Step 5: Trigger the simulation compensation mechanism when the simulation error coefficient exceeds 0.5.

[0079] The above-mentioned 3D logic circuit virtual simulation method has the following application system: including an intelligent monitoring module, a data integration module, an intelligent algorithm module, and a simulation compensation module;

[0080] The intelligent monitoring module is used to integrate the intelligent monitoring system, connect to the manufacturer's system database, and establish an environmental monitoring system.

[0081] The data integration module acquires equipment operation data, material performance data, and environmental monitoring parameters based on the intelligent monitoring module;

[0082] The intelligent algorithm module is used to integrate artificial intelligence algorithms to calculate simulation error coefficients;

[0083] The simulation compensation module implements a simulation compensation mechanism based on the simulation error coefficient.

[0084] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A 3D logic circuit virtual simulation method, characterized in that, Includes the following steps: Step 1: Install an intelligent monitoring system to monitor the operating data of the manufacturing equipment and integrate the equipment operating data; Step 2: Obtain material performance data from the manufacturer's system database and integrate the obtained material performance data; Step 3: Establish an environmental monitoring system to acquire environmental monitoring parameters and integrate the environmental monitoring parameters; Step 4: Calculate the simulation error coefficient based on artificial intelligence algorithms, integrating equipment operation data, material performance data, and environmental monitoring parameters; Step 5: Trigger the simulation compensation mechanism based on the simulation error coefficient.

2. The 3D logic circuit virtual simulation method according to claim 1, characterized in that: The equipment operation data includes printhead position, extruder actual speed, substrate processing size deviation, 3D stacking assembly deviation, and pick-and-place machine positioning repeatability. The integrated numbering expression for the equipment operation data is as follows: SYsj=[Sdw, Sjz, Sjc, Spp, Stj] In the expression, SYsj represents the equipment operation data, Sdw represents the printhead position in the equipment operation data, Sjz represents the actual speed of the extruder in the equipment operation data, Sjc represents the substrate processing size deviation in the equipment operation data, Spp represents the 3D stacking assembly deviation in the equipment operation data, and Stj represents the positioning repeatability accuracy of the pick-and-place machine in the equipment operation data.

3. The 3D logic circuit virtual simulation method according to claim 1, characterized in that: The material performance data includes linear shrinkage rate, coefficient of thermal expansion, and molding compound curing shrinkage rate. The integrated numbering expression for the material performance data is as follows: CXsj = [Cxs, Crp, Cgs] In the expression, CXsj represents the material performance data, Cxs represents the linear shrinkage rate in the material performance data, Crp represents the coefficient of thermal expansion in the material performance data, and Cgs represents the curing shrinkage rate of the molding compound in the material performance data.

4. The 3D logic circuit virtual simulation method according to claim 1, characterized in that: The environmental monitoring parameters include: ambient temperature, ambient relative humidity, standard ambient temperature, and standard ambient relative humidity. The integrated numbering expression for the environmental monitoring parameters is as follows: HJsj=[Hwd, Hsd, Hbw, Hbs] In the expression, HJsj represents the environmental monitoring parameter, Hwd represents the ambient temperature among the environmental monitoring parameters, Hsd represents the ambient relative humidity among the environmental monitoring parameters, Hbw represents the standard ambient temperature among the environmental monitoring parameters, and Hbs represents the standard ambient relative humidity among the environmental monitoring parameters.

5. The 3D logic circuit virtual simulation method according to claim 2, characterized in that: The formula for calculating the simulation error coefficient is as follows: W = μ·Sy + θ·Cy + ρ·Hy In the formula, W represents the simulation error coefficient, Sy represents the equipment operation influence coefficient, μ represents the weight of the equipment operation influence coefficient, Cy represents the material performance influence coefficient, θ represents the weight of the material performance influence coefficient, Hy represents the environmental interference index, and ρ represents the weight of the environmental interference index.

6. The 3D logic circuit virtual simulation method according to claim 2, characterized in that: The formula for calculating the equipment operation impact coefficient is as follows: In the formula, Sy represents the equipment operation influence coefficient, Si represents the i-th parameter in the equipment operation data, Zi represents the preset parameter of the i-th parameter in the equipment operation data, Ci represents the allowable difference of the i-th parameter in the equipment operation data, and ai represents the weight of the i-th parameter in the equipment operation data.

7. The 3D logic circuit virtual simulation method according to claim 3, characterized in that: The formula for calculating the influence coefficient of the material properties is as follows: In the formula, Cy represents the material performance influence coefficient, Yj represents the j-th parameter in the material performance data, Fj represents the preset parameter of the j-th parameter in the material performance data, Cj represents the allowable difference of the j-th parameter in the material performance data, and βi represents the weight of the j-th parameter in the material performance data.

8. The 3D logic circuit virtual simulation method according to claim 4, characterized in that: The formula for calculating the environmental disturbance index is as follows: In the formula, Hy represents the environmental disturbance index, M represents the allowable difference of environmental temperature, N represents the allowable difference of environmental relative humidity, δ1 represents the weight of environmental temperature, and (1-δ1) represents the weight of environmental relative humidity.

9. A 3D logic circuit virtual simulation method according to claim 5, characterized in that: The simulation compensation mechanism is triggered when the simulation error coefficient exceeds 0.

5.

10. A 3D logic circuit virtual simulation system, applied to the 3D logic circuit virtual simulation method according to any one of claims 1 to 9, characterized in that: It includes an intelligent monitoring module, a data integration module, an intelligent algorithm module, and a simulation compensation module; The intelligent monitoring module is used to integrate the intelligent monitoring system, connect to the manufacturer's system database, and establish an environmental monitoring system. The data integration module acquires equipment operation data, material performance data, and environmental monitoring parameters based on the intelligent monitoring module. The intelligent algorithm module is used to integrate artificial intelligence algorithms to calculate simulation error coefficients; The simulation compensation module implements a simulation compensation mechanism based on the simulation error coefficient.

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