A Logic Test Chip for Optimal Test and Diagnosis
Creators: name: Niewenhuis, Benjamin T.
Publisher: name: Carnegie Mellon University
Year: 2018
Subjects: subject: Computer Engineering
subject: 90699 Electrical and Electronic Engineering not elsewhere classified
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: The benefits of the continued progress in integrated circuit manufacturing have been numerous, most notably in the explosion of computing power in devices ranging from cell phones to cars. Key to this success has been strategies to identify, manage, and mitigate yield loss. One such strategy is the use of test structures to identify sources of yield loss early in the development of a new manufacturing process. However, the aggressive scaling of feature dimensions, the integration of new materials, and the increase in structural complexity in modern technologies has challenged the capabilities of conventional test structures. To help address these challenges, a new logic test chip, called the Carnegie Mellon Logic Characterization Vehicle (CM-LCV), has been developed. The CM-LCV utilizes a two- dimensional array of functional unit blocks (FUBs) that each implement an innovative functionality. Properties including fault coverage, logical and physical design features, and fault distinguishability are shown to be composable within the FUB array; that is, they exist regardless of the size and composition of the FUB array. A synthesis ow that leverages this composability to adapt the FUB array to a wide range of test chip design requirements is presented. The connection between the innovative FUB functionality and orthogonal Latin squares is identified and used to analyze the universe of possible FUB functions. Two additional variants to the FUB array are also developed: heterogenous FUB arrays utilize multiple FUB functions to improve the synthesis ow performance, while pipelined FUB arrays incorporate sequential circuit elements (e.g., ip- ops and latches) that are absent from the original combinational FUB array. In addition to the design of the CM-LCV, methods for testing it are presented. Techniques to create minimal sets of test patterns that exhaustively exercise each FUB within the FUB array are developed. Additional constraints are described for the heterogenous and pipelined FUB arrays that allow these techniques to be applied for both variant FUB arrays. Furthermore, a simple built-in self test (BIST) scheme is described and applied to a reference design, resulting in a 88.0% reduction in the number of test cycles required without loss in fault coverage. A hierarchical FUB array diagnosis methodology (HFAD) is also presented for the CM- LCV that leverages its unique properties to improve performance for multiple defects. Experiments demonstrate that this HFAD methodology is capable of perfect accuracy in 93.1% of simulations with two injected faults, an improvement on the state-of-the-art commercial diagnosis. Additionally, silicon fail data was collected from a CM-LCV manufactured using a 14nm process by an industry partner. A comparison of the diagnosis results for the 1,375 fail logs examined shows that the HFAD methodology discovers additional defects during multiple defect diagnosis that the commercial tool misses for 40 of the diagnosed fail logs. Examination of these cases shows that the additional defects found by the HFAD methodology can result in improved diagnosis confidence and more precise descriptions of the defect behavior(s). The contributions of this dissertation can thus be summarized as the description of the design, test, and diagnosis of a new logic test chip for use in yield learning during process development. This CM-LCV can be adapted to meet a wide range of test chip requirements, can be efficiently and rigorously tested, and exhibits properties that can be used to improve diagnosis outcomes. All of these claims are validated through both simulated experiments and silicon data.