Detecting Malicious Modifications in Semiconductor Devices
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Solution Overview
Problem
The semiconductor industry faces challenges in detecting malicious modifications, such as hardware trojans, in semiconductor devices due to their large and complex designs, which can be tampered with during the manufacturing process, making it difficult to distinguish between defects and malicious alterations using existing verification techniques.
Innovation Solution
A method is introduced that uses regular, canonical structures to exhaustively verify unknown devices against known-correct golden reference devices by applying automatically generated patterns, compressing responses with a signature analyzer to produce CRC codes, and comparing them to ensure authenticity, with specific design constraints like scannable flip-flops and fenced non-digital logic to prevent indeterminate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive verification testing is applied to detect malicious modifications, then detection capability improves, but device complexity increases due to additional test structures and procedures
Solution Approach 1:
The verification system is segmented into distinct functional modules: pattern generation unit, stimulus application unit, response capture unit, CRC computation unit, and comparison unit. Each module performs a specific function in the verification chain, making the overall complex testing process manageable and implementable through standardized components.
Solution Approach 2:
The invention uses a golden model (copy) of the original device design to generate expected response patterns. This golden reference is used to create test vectors and expected CRC values, allowing verification without requiring physical access to the original unmodified device. The golden model serves as a trusted reference for comparing against potentially modified devices.
2Adaptability or versatility
If device designs become larger and more complex to meet performance requirements, then functional capability improves, but vulnerability to malicious modifications increases
Solution Approach 1:
The verification system implements feedback by comparing the actual device response against expected responses derived from the golden model. The CRC computation provides a condensed feedback mechanism where the entire device response is reduced to a checksum value that can be quickly compared to detect any deviations caused by malicious modifications.
Solution Approach 2:
The system performs preliminary verification by applying test patterns and computing expected CRC values using the golden model before actual device deployment. This advance preparation creates a baseline of expected behavior that can be used to quickly identify malicious modifications without requiring complex real-time analysis during device operation.
3Productivity
If existing verification techniques are used on modified devices, then testing speed maintains, but ability to distinguish defects from malicious alterations deteriorates
Solution Approach 1:
The invention changes the verification parameter from simple pass/fail testing to CRC-based response validation. By computing and comparing cyclic redundancy check values of device responses against golden model expectations, the system maintains fast testing speeds while significantly improving the ability to detect malicious modifications that would otherwise be indistinguishable from normal variations.
Data Source
AI summary
A computer-implemented method, and system, for detecting modification of a semiconductor device includes generating and applying an exhaustive first set of patterns to a netlist golden model of a golden device. The exhaustive first set of patterns is developed by a pseudorandom number generator. Applying the patterns to stimulate the device produces a first response serial bit stream in relation to logical composition of the golden model. A signature analyzer compresses the total output to provide a first cyclic redundancy code or answer. The same exhaustive set of patterns can be provided to stimulate the model of an unknown device. The unknown device is shown to be identical to the golden device if its answer matches that of the golden device and modified if its answer does not match that of the golden device.