Modern electronics testing has a legacy of more than 40 years. The introduction of new technologies, especially nanometer technologies with 90nm or smaller geometry, has allowed the semiconductor industry to keep pace with the increased performance-capacity demands from consumers. As a result, semiconductor test costs have been growing steadily and typically amount to 40% of today's overall product cost. 

This book is a comprehensive guide to new VLSI Testing and Design-for-Testability techniques that will allow students, researchers, DFT practitioners, and VLSI designers to master quickly System-on-Chip Test architectures, for test debug and diagnosis of digital, memory, and analog/mixed-signal designs.

KEY FEATURES
* Emphasizes VLSI Test principles and Design for Testability architectures, with numerous illustrations/examples.
* Most up-to-date coverage available, including Fault Tolerance, Low-Power Testing, Defect and Error Tolerance, Network-on-Chip (NOC) Testing, Software-Based Self-Testing, FPGA Testing, MEMS Testing, and System-In-Package (SIP) Testing, which are not yet available in any testing book.
* Covers the entire spectrum of VLSI testing and DFT architectures, from digital and analog, to memory circuits, and fault diagnosis and self-repair from digital to memory circuits.
* Discusses future nanotechnology test trends and challenges facing the nanometer design era; promising nanotechnology test techniques, including Quantum-Dots, Cellular Automata, Carbon-Nanotubes, and Hybrid Semiconductor/Nanowire/Molecular Computing.
* Practical problems at the end of each chapter for students.

System-on-Chip Test Architectures: Nanometer Design for Testability

Increasing levels of integration and high speeds of operation have made the problem of testing complex systems-on-packages (SOPs) very difficult. Testing packages with multigigahertz RF and optical components is even more difficult as external tester costs tend to escalate rapidly beyond 3 GHz. The extent of the problem can be gauged by the fact that test cost is approaching almost 40% of the total manufacturing cost of these packages. To alleviate test costs, various solutions relying on built-off test (BOT) and built-in test (BIT) of embedded high-speed components of SOPs have been developed. These migrate some of the external tester functions to the tester load board (BOT) and to the package and the die encapsulated in the package (BIT) in an "intelligent" manner. This paper provides a discussion of the emerging BOT and BIT schemes for embedded high-speed RF/analog/mixed-signal circuits in SOPs. The pros and cons of each scheme are discussed and preliminary available data on case studies are presented.

Low-cost test of embedded RF/analog/mixed-signal circuits in SOPs

We discuss a fault diagnosis scheme for analog integrated circuits. Our approach is based on an assemblage of learning machines that are trained beforehand to guide us through diagnosis decisions. The central learning machine is a defect filter that distinguishes failing devices due to gross defects (hard faults) from failing devices due to excessive parametric deviations (soft faults). Thus, the defect filter is key in developing a unified hard/soft fault diagnosis approach. Two types of diagnosis can be carried out according to the decision of the defect filter: hard faults are diagnosed using a multi-class classifier, whereas soft faults are diagnosed using inverse regression functions. We show how this approach can be used to single out diagnostic scenarios in an RF low noise amplifier (LNA).

Fault diagnosis of analog circuits based on machine learning

A method for diagnosing process parameter variations from measurements in analog circuits. The diagnosability conditions for the accurate computation of device parameters are extended in the presence of measurement noise. In case this diagnosability condition is not met by standard test signals, a method is provided for automatically generating optimized tests that enable the computation of device parameters. The test generator explicitly optimizes the ability to compute device parameters from the test response. A cause-effect analysis engine is provided to diagnose the cause of variation in IC performance metrics in terms of the variation in device parameter values. Once the cause of parametric yield loss is diagnosed in terms of device parameters variations, the information can be used by process engineers to tune the manufacturing process to improve yield.

Method for diagnosing process parameter variations from measurements in analog circuits

In this paper, we present a parametric fault diagnosis approach for analog/RF circuits based on a Bayesian framework. The Bayesian fault diagnosis requires extensive statistical profiling which is enabled by a an efficient hierarchical process variability analysis. Both DC and AC parameters are used as measurements to provide maximum diagnostic resolution. A sensitivity guided test input selection scheme is used to determine the measurement attributes that are most likely to distinguish among the faults. Fault dictionaries are constructed using parametric faults at the transistor level that have both marginal and higher deviations. During the diagnosis step, additional online profiling helps increase the diagnostic resolution. Experiments on a transistor level amplifier circuit confirms that the approach is accurate in terms of statistical attributes and most deviations in layout and process level parameters can be correctly diagnosed.

Parametric fault diagnosis for analog circuits using a Bayesian framework

With the increasing complexity of manufacturing processes and the shrinking of device geometries, the performance metrics of integrated circuits (ICs) are becoming increasingly sensitive to random fluctuations in the manufacturing process. We propose a diagnosis methodology that can be used to infer the cause(s) of variations in performance of analog ICs. The methodology consists of (a) a device parameter computation technique which is used to compute the device parameters of an IC from measurements made on it and (b) a cause-effect analysis module that is used to compute the cause of the variation in performance metrics of a given set of ICs. Simulation results to demonstrate the effectiveness of the technique are presented.

S. Chatterjee

Papers

Test generation based diagnosis of device parameters for analog circuits