Netlist Hierarchy Extraction via Landmark Pattern Matching
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Solution Overview
Problem
As integrated circuits (ICs) become larger and more complex, it is increasingly difficult to understand their functionality from raw netlists at the element level, and existing methods lack the ability to efficiently convert these into higher-level abstractions or behavioral models necessary for repair, comparison, or detection of malicious circuitry.
Innovation Solution
A pattern matching system that converts netlists into higher-level abstractions using optimization techniques, such as landscaping and search optimization algorithms, to identify landmark structures and patterns, thereby extracting hierarchy and functionality from low-level netlists, allowing for the creation of human-readable models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If destructive and non-destructive techniques such as SEM imaging, IR and X-ray techniques are used to image an integrated circuit and produce a low level netlist, then the circuitry can be visualized and a netlist can be obtained, but the extracted netlist is extremely hard to understand and requires conversion to higher level form
Solution Approach 1:
The patent segments the complex low-level netlist into hierarchical groups and clusters representing functional blocks. The system divides the circuit into manageable sections with common functions, allowing users to understand the circuit at multiple levels of abstraction rather than being overwhelmed by individual elements.
Solution Approach 2:
The patent introduces an intermediary processing system that translates the raw netlist output from imaging techniques into human-readable hierarchical representations. This intermediary layer converts element-level data into functional block descriptions, making the circuitry understandable without losing the detailed information.
2Loss of information
If pattern matching algorithms are used to convert netlists to higher level forms, then the functionality can be understood and compared to datasheets, but the search process may produce false positives and requires optimization
Solution Approach 1:
The patent implements feedback mechanisms where the pattern matching results are validated against multiple criteria including electrical characteristics, spatial relationships, and functional behavior. The system refines its pattern matching based on feedback from previous matches, adjusting parameters to reduce false positives while maintaining detection capability.
Solution Approach 2:
The patent dynamically adjusts pattern matching parameters such as similarity thresholds, cluster size criteria, and matching sensitivity based on the specific circuit being analyzed. By changing parameters adaptively, the system optimizes the balance between detecting all functional blocks and avoiding false positives.
3Productivity
If optimization techniques such as landscaping and search optimization algorithms are used to identify landmark structures, then the search convergence is optimized and false positives are minimized, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing the netlist to identify obvious landmark structures and common functional patterns before applying complex optimization algorithms. By preparing the data in advance and removing easily identifiable elements, the subsequent optimization processes work more efficiently with reduced computational burden.
Solution Approach 2:
The patent applies different levels of algorithmic complexity to different regions of the circuit based on their characteristics. Simple functional blocks use basic pattern matching, while complex regions receive more sophisticated optimization treatment. This localized approach optimizes overall performance without unnecessarily complicating the entire system.
Data Source
AI summary
A system and method for adding hierarchy to a netlist. A netlist is received and converted into a connected graph. Location parameters for the nodes of the connected graph are mapped onto the connected graph. Landmark structures are identified in the connected graph, wherein identifying includes recording a location associated with each landmark structure. Patterns are searched for in the connected graph, wherein searching proceeds outward from an anchor defined by the location of each of the identified landmark structures.