As semiconductor technology advances, small delay defects (SDDs) have become a major concern in System-on-Chip (SoC) testing due to shrinking timing margins. Moreover, diagnosing SDDs is getting even more challenging due to their subtle impact on circuit timing and the existence of process variations. This paper presents an SDD diagnosis method integrating timing-aware fault simulation with injected delay selection and a mismatch-weighted (MW) score method to enhance diagnostic accuracy. Rather than relying on fixed delay values, the proposed method determines injected delays based on the slack of transition paths, generating multiple delay sizes to improve fault simulation resolution. The MW score calculation adjusts contributions of failures, enhancing defect ranking and mitigating process variation effects. Experimental results demonstrate that the proposed method significantly improves SDD localization and reduces the number of fault candidates, outperforming conventional approaches in both accuracy and efficiency.

As semiconductor manufacturing processes continue to shrink, the impact of small-delay defects becomes increasingly significant. Traditional automatic test pattern generation (ATPG) methods often fail to detect these defects, as they are masked by critical paths. This work proposes a Timing-Partitioned Small-Delay-Defect (TPSDD) methodology to address this issue. By analyzing timing path delays and grouping transition paths based on their delay sizes, ATPG patterns can be generated to specifically test these groups, revealing hidden small-delay defects. Additionally, techniques for identifying outlier dies and defect candidates are presented, validated through testing on the N2 process node. Our silicon results confirm the effectiveness of TPSDD in detecting small-delay defects, enhancing reliability for applications requiring high robustness.

The eBPF technology is widely used for many applications, including tracing, packet filtering, network usage monitoring, and so on. The versatility of eBPF allows the kernel’s capabilities to be extended without needing to modify source code or load kernel modules. However, the eBPF subsystem may introduce new bugs that could lead to crashes, data loss, and other issues that can negatively impact system stability, reliability, availability, security, and overall performance. Specifically, locking violations, which occur when locks are not used correctly, can lead to problems like deadlocks and system hangs. Since eBPF operates at the kernel level, errors here have far-reaching consequences.To tackle this issue, we present Spinner, a tool for detecting locking violations in the eBPF runtime. Spinner uses static analysis to (1) detect cases of context confusion where incorrect locking primitives are used in eBPF helper functions given their execution context, and (2) identify locks in helper functions that can be called recursively using nested eBPF programs. Both of these situations could result in deadlocks. So far, Spinner has identified 34 locking violation bugs in the eBPF subsystem in Linux, only 5 of which were previously found by Syzbot.

The Linux kernel is an important part of the Trusted Computing Base (TCB) of a mobile device using the Android OS, making it attractive to attackers. While all vulnerabilities in the kernel are important, those that are directly reachable by untrusted programs pose a grave threat. This paper introduces Sifter, a solution for protecting security-critical kernel modules, i.e., those modules that are directly exposed to untrusted programs. Sifter's key approach is the use of fine-grained, highly-selective filters to reduce the attack surface of these kernel modules and make their vulnerabilities unreachable for untrusted programs. The key observation in Sifter is that there are rich patterns in how legitimate programs issue syscalls to these kernel modules; thus, one can generate filters that only allow such syscall patterns, and as a result mitigate vulnerabilities (including zero-day ones) that could only be exploited by the use of unorthodox syscall patterns.

Selective symbolic execution (SSE) is a powerful program analysis technique for exploring multiple execution paths of a program. However, it faces a challenge in analyzing programs with environments that cannot be modeled nor virtualized. Examples include OS services managing I/O devices, software frameworks for accelerators, and specialized applications. We introduce Mousse, a system for analyzing such programs using SSE. Mousse uses novel solutions to overcome the above challenge. These include a novel process-level SSE design, environment-aware concurrent execution, and distributed execution of program paths. We use Mousse to comprehensively analyze five OS services in three smartphones. We perform bug and vulnerability detection, taint analysis, and performance profiling. Our evaluation shows that Mousse outperforms alternative solutions in terms of performance and coverage.

IOT has seen countless potential applications that can improve our lives dramatically, but after years of efforts by numerous companies and organizations, the beautiful dreams are yet to be realized. The main obstacles are cost and energy consumption constraints of the devices and systems, which still cannot be contained. As a step forward in improving the reliability and reducing the cost and energy consumption of IOT devices and systems, we propose a high-level model for efficient design-space exploration, which is called the symbiotic system (SS) model. Based on that, we propose a quorum-sensing model for SS to improve the reliability of IOT systems that contain subsystems. Experimental result shows that, with the SS approach, the lifetime of an example IOT storage system with 50K failure-in-time/Mb nodes can be extended by 74.41 %. In addition, to double the lifetime of the system, only 5% additional repair resource is required. In short, by the SS model, it is possible for an IOT system to achieve low cost, low energy consumption, and high reliability.

IOT has seen countless potential applications that can improve our lives dramatically, but after years of efforts by numerous companies and organizations, the beautiful dreams are yet to be realized. The main obstacles are cost and energy consumption constraints of the devices and systems, which still cannot be contained. As a step forward in improving the reliability and reducing the cost and energy consumption of IOT devices and systems, at ITC-Asia17 we proposed a high-level model for efficient design-space exploration, which is called the symbiotic system (SS) model. Based on that, we also proposed a quorum-sensing model for SS to improve the reliability of IOT systems that contain subsystems. Preliminary experimental result shows that it is possible for an IOT system to achieve low cost, low energy consumption, and high reliability. In this extended work we discuss more evaluation results, and show that by using quorum-sensing-based peer-repair, up to 97% of the faulty devices can be repaired even when the raw yield is only 30%. It shows that by adopting the proposed approach, there is hope in eliminating production test of symbiotic IOT devices in the future.