CAREER: Spectral Techniques for Functional Testing of Sequential Circuits and System-On-A-Chip

职业：用于时序电路和片上系统功能测试的频谱技术

• 批准号: 0093042
• 负责人: Michael Hsiao
• 金额: $ 32.67万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-02-01 至 2001-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0093042&HistoricalAwards=false
• 关键词: CAREER; Spectral; Techniques; Functional; Testing

While current circuit testing techniques are limited by the increasing circuit size, the proposed research explores a new concept of spectrum-based test generation to tackle testing of increasingly large and complex circuits. A state-of-the-art spectrum-based technique for chip functional testing is being explored that achieves extremely high fault coverages in short execution times. In this approach, the circuit is viewed as a system identifiable by its input-output characteristics instead of a traditional netlist of primitives. Spectral characteristics are extracted and analyzed for the input/output signals of the system under test, and the resulting data are used to predict and construct intelligent test patterns. In addition, this functional spectral knowledge also provides new means for built-in-self-test (BIST), applicable also for system-on-a-chip. This research addresses the relevant issues in the spectrum-based testing framework: (a) the development of various techniques for spectral extraction for a given circuit; (b) the analysis of different spectral bases (orthogonal and non-orthogonal) and how they can influence spectral characteristics for testing; and (c) the application of transform domain to identify dominant frequency components to generate intelligent patterns.

目前的电路测试技术受到电路尺寸增加的限制，本研究探索了基于频谱的测试生成的新概念，以解决日益庞大和复杂的电路的测试。一种最先进的基于频谱的芯片功能测试技术正在开发中，该技术可以在短时间内实现极高的故障覆盖率。在这种方法中，电路被看作是一个由其输入输出特性而不是传统的原语网表来识别的系统。对被测系统的输入/输出信号进行光谱特征提取和分析，所得数据用于预测和构建智能测试模式。此外，这种功能光谱知识还为内置自检（BIST）提供了新的手段，也适用于片上系统。本研究解决了基于频谱的测试框架中的相关问题：(a)开发了针对给定电路的各种频谱提取技术；(b)分析不同的光谱基（正交和非正交），以及它们如何影响测试的光谱特性；(c)利用变换域识别主频率分量，生成智能模式。