ISO 26262 requires classifying random hardware faults based on their effects (safe, detected, or undetected) within integrated circuits used in automobiles. In general, this classification is addressed using expert judgment and a combination of tools. However, the growth of integrated circuit complexity creates a huge fault space; hence, this form of fault classification is error prone and time consuming. Therefore, an automated and systematic approach is needed to target hardware fault classification in automotive systems on chips (SoCs), considering the application software. This work focuses on identifying safe faults: the proposed approach utilizes coverage analysis to identify candidate safe faults considering all the constraints coming from the application. Then, the behavior of the application software is modeled so that we can resort to a formal analysis tool. The proposed technique is evaluated on the AutoSoC benchmark running a cruise control application. Resorting to our approach, we could classify 20%, 11%, and 13% of all faults in the central processing unit (CPU), universal asynchronous receiver–transmitter (UART), and controller area network (CAN) as safe faults, respectively. We also show that this classification can increase the diagnostic coverage of software test libraries targeting the CPU and CAN modules by 4% to 6%, increasing the achieved testable fault coverage.

In order to match the strict reliability requirements mandated by regulations and standards adopted in the automotive sector, as well as other domains where safety is a major concern, the in-field testing of the most critical devices, including microcontrollers and systems on chip, is a crucial task. Since the controller area network (CAN) bus is widely used in the automotive domain, the corresponding controller ubiquitously appears in all these devices. This paper presents a generic and systematic methodology to develop an effective in-field test procedure for CAN controllers based on a functional approach (i.e., on the adoption of self-test libraries). The method can be customized to match the requirements coming from different scenarios, and allows the test engineer to maximize the achieved fault coverage in terms of structural faults in the different cases. The experimental results we gathered on a representative CAN controller model show that, given two typical testing scenarios, we are able to detect $84.28\%$ and $87.62\%$ of stuck-at faults, respectively, hence demonstrating the effectiveness of the proposed approach.

The requirements of ISO26262 for the development of safety-critical Integrated Circuits (IC) demand substantial efforts on fault analysis for safety metrics evaluation. Failing to achieve the required conditions entails modifications to the circuit, additional iterations through critical design phases, and consequently extra costs and delays. For that reason, providing accurate methods to estimate safety metrics is of great importance. This paper proposes a methodology that can efficiently and precisely estimate the safety metrics of Automotive designs. The technique is based on the characterization of a netlist to determine how hardware components contribute to fault propagation. Also, by examining the test stimuli applied during simulation, we can rank Workloads/Testbenches according to their fault detection coverage. The approach was verified running fault injection campaigns on distinct gate-level hardware designs, including an Automotive CPU. Our results show that the fault detection coverage can be estimated with an average error rate of 3% at up to 20X faster execution times when compared to the traditional campaigns. Hence the methodology provides an efficient and cost-effective mechanism to support engineers in a confident design space exploration.