Acoustic Microwave Filter Modeling With LCR Resonator Optimization
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Solution Overview
Problem
Current acoustic microwave filter design processes are inefficient and inaccurate, leading to poor correlations between simulations and measurements due to errors in training mask measurements, which can result in compromised filter performance.
Innovation Solution
A method for designing acoustic microwave filters using a modeled filter circuit design with electrical circuit models that include acoustic resonant elements, such as SAW, BAW, or FBAR, and MEMS resonators, optimized to meet frequency response requirements by simulating physical and electrical models and modifying parameters to match desired characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional filter design processes are used, then design simplicity is maintained, but modeling accuracy and correlation between simulations and measurements deteriorate
Solution Approach 1:
The filter design process is segmented into distinct stages: creating an initial filter design, generating a training mask, measuring S-parameters, extracting pole-zero data, and creating equivalent circuit models. This segmentation allows each stage to be optimized independently, improving overall modeling accuracy while maintaining manageable complexity through systematic progression.
Solution Approach 2:
A training mask is generated in advance containing multiple resonators with varying characteristics (different Q-factors, resonant frequencies, and coupling coefficients). This preliminary action provides a comprehensive dataset for measuring S-parameters and extracting pole-zero information, which establishes accurate equivalent circuit models before the actual filter design, thereby improving modeling accuracy.
2Reliability
If training mask measurements are used, then filter performance can be optimized, but errors in measurements lead to poor correlation between simulations and measurements
Solution Approach 1:
The process uses measured S-parameters from the training mask to extract pole-zero data, which then feeds into creating equivalent circuit models. These models are used to simulate filter performance, and the simulation results are compared against actual measurements to validate accuracy. This feedback loop ensures that measurement errors are identified and corrected, improving both filter performance and simulation-measurement correlation.
Solution Approach 2:
Physical resonators in the training mask are replicated in equivalent circuit models that capture their electrical characteristics. By creating accurate electrical copies of the physical devices through pole-zero extraction and circuit synthesis, the models can predict filter behavior with high precision, improving simulation-measurement correlation while maintaining reliable filter performance.
3Manufacturing precision
If resonators with very low internal resistance are used, then filter selectivity is improved, but resonator size and cost increase
Solution Approach 1:
The invention varies key resonator parameters including Q-factor, resonant frequency, and coupling coefficient across the training mask. By systematically changing these parameters, the process identifies optimal resonator configurations that achieve the required filter selectivity without unnecessarily minimizing resistance, thereby avoiding excessive size and cost while maintaining manufacturing precision.
Solution Approach 2:
Instead of requiring all resonators to have extremely low internal resistance, the process uses a combination of resonators with varying Q-factors. Some resonators provide the necessary selectivity while others contribute to overall filter response shaping. This partial application of low-resistance design achieves the required selectivity without the excessive size and cost that would result from uniformly minimizing resistance across all resonators.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables more efficient and accurate modeling of acoustic microwave filters, improving the correlation between simulations and measurements, and enhancing the performance of filters by optimizing resonator characteristics and frequency responses.
Implementation Method 1
a plurality of resonators, which store energy very efficiently at a resonant frequency
Implementation Method 2
acoustic resonant element may, e.g., be one of a surface acoustic wave (SAW) resonator
Implementation Method 3
a bulk acoustic wave (BAW) resonator
Implementation Method 4
Each resonator may include a pair of interdigitated transducers (IDTs) formed over a piezoelectric substrate
Data Source
AI summary
A method for designing a narrowband acoustic wave microwave filter including: generating a modeled filter circuit design having circuit elements including an acoustic resonant element defined by an electrical circuit model that includes a parallel static branch, a parallel motional branch, and one or both of a parallel Bragg Band branch that models an upper Bragg Band discontinuity and a parallel bulk mode function that models an acoustic bulk mode loss; and generating a final circuit design. Generating the final circuit design includes optimizing the modeled filter circuit design to generate an optimized filter circuit design; comparing a frequency response of the optimized filter circuit design to requirements; selecting the optimized filter circuit design for construction into the actual acoustic microwave filter based on the comparison; and transforming the optimized filter circuit design to a design description file for input to a construction process.