The ever growing demand for functionally robust and error-free industrial electronics necessitates the development of techniques that will prohibit the propagation of functional errors to the final tape-out stage. This paramount requirement in the semiconductor world is imposed by the equivocal observation that functional errors slipping to silicon production introduce immense amounts of cost and jeopardize chip release dates. Functional verification and debugging are burdened with the tedious task of guaranteeing logic functionality early in the design cycle. In this paper, we present an automated method for the very first stage of functional debugging, called failure triage. Failure triage is the task of analyzing large sets of failures, grouping together those that are likely to be caused by the same design error, and then allocating those groups to the appropriate engineers for fixing. The introduced framework instruments techniques from the machine learning domain combined with the root cause analysis power of modern SAT-based debugging tools, in order to exploit information from error traces and bin the corresponding failures using clustering algorithms. Preliminary experimental results indicate an average accuracy of 93 % for the proposed failure triage engine, which corresponds to a 43 % improvement over conventional automated methods.

With the growing size of modern designs and more strict time-to-market constraints, design errors unavoidably escape pre-silicon verification and reside in silicon prototypes. As a result, silicon debug has become a necessary step in the digital integrated circuit design flow. Although embedded hardware blocks, such as scan chains and trace buffers, provide a means to acquire data of internal signals in real time for debugging, there is a relative shortage in methodologies to efficiently analyze this vast data to identify root-causes. This paper presents an automated software solution that attempts to fill-in the gap. The presented techniques automate the configuration process for trace-buffer based hardware in order to acquire helpful information for debugging the failure, and detect suspects of the failure in both the spatial and temporal domain.

We propose new hardware and software techniques for FPGA functional debug that leverage the inherent reconfigurability of the FPGA fabric to reduce functional debugging time. The functionality of an FPGA circuit is represented by a programming bitstream that specifies the configuration of the FPGA's internal logic and routing. The proposed methodology allows different sets of design internal signals to be traced solely by changes to the programming bitstream followed by device reconfiguration and hardware execution. Evidently, the advantage of this new methodology vs. existing debug techniques is that it operates without the need of iterative executions of the computationally-intensive design re-synthesis, placement and routing tools. In essence, with a single execution of the synthesis flow, the new approach permits a large number of internal signals to be traced for an arbitrary number of clock cycles using a limited number of external pins. Experimental results using commercial FPGA vendor tools demonstrate productivity (i.e. run-time) improvements of up to 30× vs. a conventional approach to FPGA functional debugging. These results demonstrate the practicality and effectiveness of the proposed approach.

With the growing size of modern designs and more strict time-to-market constraints, design errors can unavoidably escape pre-silicon verification and reside in silicon prototypes. Due to those errors and faults in the fabrication process, silicon debug has become a necessary step in the digital integrated circuit design flow. Embedded hardware blocks, such as scan chains and trace buffers, provide a means to acquire data of internal signals in real time for debugging. However, the amount of the data is limited compared to pre-silicon debugging. This paper presents an automated software solution to analyze this sparse data to detect suspects of the failure in both the spatial and temporal domain. It also introduces a technique to automate the configuration process for trace-buffer-based hardware in order to acquire helpful information for debugging the failure. The technique takes the hardware constraints into account and identifies alternatives for signals not part of the traceable set so that their values can be restored by implications. The experiments demonstrate the effectiveness of the proposed software solution in terms of run-time and resolution.

During the very large scale integration design process, a synthesized design is often required to be modified in order to accommodate different goals. To preserve the engineering effort already invested, designers seek small logic structural transformations to achieve these logic restructuring goals. This paper proposes a systematic methodology to devise such transformations automatically. It first presents a simulation-based formulation to approximate sets of pairs of functions to be distinguished and avoid the memory/time explosion issue inherent with the original representation. Then, it uses this new data structure to devise the required transformations dynamically without the need of a static dictionary model. The methodology is applied to both combinational and sequential designs with transformations at a single or multiple locations. An extensive suite of experiments documents the benefits of the proposed methodology when compared to existing practices.

Since pre-silicon functional verification is insufficient to detect all design errors, re-spins are often needed due to malfunctions that escape into the silicon. This paper presents an automated software solution to analyze the data collected during silicon debug. The proposed methodology analyzes the test sequences to detect suspects in both the spatial and the temporal domain. A set of software debug techniques are proposed to analyze the acquired data from the hardware testing and provide suggestions for the setup of the test environment in the next debug session. A comprehensive set of experiments demonstrate its effectiveness in terms of run-time and resolution.

In the digital VLSI cycle, logic transformations are often required to modify the design to meet different synthesis and optimization goals. Logic transformations on sequential circuits are hard to perform due to the vast underlying solution space. This paper proposes an SPFD-based sequential logic transformation methodology to tackle the problem with no sacrifice on performance. It first presents an efficient approach to construct approximate SPFDs (aSPFDs) for sequential circuits. Then, it demonstrates an algorithm using aSPFDs to perform the desirable sequential logic transformations using both combinational and sequential don't cares. Experimental results show the effectiveness and robustness of the approach.

Sets of Pairs of Functions to be Distinguished (SPFD) is a functional flexibility representation method that was recently introduced in the logic synthesis domain, and promises superiority in exploring the flexibility offered by a design over all previous representation methods. In this work, we illustrate how the SPFD of a particular wire reveals information regarding the number of potential transient errors that may occur on that wire and may affect the output of the circuit. Using an SPFD-based rewiring method, we then demonstrate how to evolve a logic circuit in order to minimize the total number of potential transient errors in the circuit and, consequently, reduce its Soft Error Rate (SER) while controlling the effect on the rest of the design parameters, such as area, power, delay, and testability. Experimental results on ISCAS'89 and ITC'99 benchmark circuits indicate that the SER can be reduced at no additional overhead to any of the design parameters.

In the digital VLSI cycle, a netlist is often modified to correct design errors, perform small specification changes or implement incremental rewiring-based optimization operations. Most existing automated logic rectification tools use a small set of predefined logic transformations when they perform such modifications. This paper first shows that a small set of predefined transformations may not allow rectification to exploit the full potential of the design. Then, it proposes an automated simulation-based methodology to "approximate" sets of pairs of functions to be distinguished (SPFDs) and avoid the memory/time explosion problem. This representation is used by a SAT-based algorithm that devises appropriate logic transformations to fix a design. The SAT method is later complemented by a greedy one that improves on runtime performance. An extensive suite of experiments documents the added potential of the proposed rectification methodology.

Rewiring has been used extensively for optimizing the area, the power consumption, the delay, and the testability of a circuit. In this work, we demonstrate how rewiring can also be used for reducing the soft error rate (SER). We employ an ATPG-based rewiring method to generate functionally-equivalent yet structurally-different implementations of a logic circuit based on simple transformation rules. This rewiring capability, along with an off-the-shelf method for assessing the SER of a circuit, enable the integration of the SER in a unified search algorithm that iteratively evolves the design in order to satisfy a given set of objectives. Experimental results on ISCAS'89 and ITC'99 benchmark circuits verify that rewiring can indeed be successfully used to reduce the SER of a circuit and, thus, it facilitates a design-space exploration framework for trading off area, power consumption, delay, testability, and SER

In the design cycle of high-performance integrated circuits, it is common that certain components are designed directly at the transistor level. This level of design representation may not be appropriate for test generation tools that usually require a model expressed at the gate level. Logic extraction is a key step in test model generation to produce a gate-level netlist from the transistor-level representation. This is a semi-automated process which is error-prone. Once a test model is found to be erroneous, manual debugging is required, which is a resource-intensive and time-consuming process. This paper presents an in-depth analysis of typical sets of extraction errors found in the test model representations of the pipelines in high-performance designs today. It also develops an automated debugging solution for single extraction errors for pipelines with no state equivalence information. A suite of experiments on circuits with similar architecture to that found in the industry confirms the fitness and practicality of the solution.

Test model generation is common in the design cycle of custom made high performance low power designs targeted for high volume production. Logic extraction is a key step in test model generation to produce a logic level netlist from the transistor level representation. This is a semi-automated process which is error prone. The paper analyzes typical extraction errors applicable to clocking schemes seen in high-performance designs today. An automated debugging solution for these errors in designs with no state equivalence information is also presented. A suite of experiments on circuits with similar architectures to those found in the industry confirm the fitness and practicality of the solution.

Test model generation is crucial in the test generation process of a high-performance design targeted for large volume production. A key process in test model generation requires the extraction of a gate-level (logic) model from the transistor level representation of the circuit under test. Logic extraction is an error prone process due to extraction tool limitations and due to the human interference. Errors introduced by extraction require manual debugging, a resource intensive and time consuming task. This paper presents a set of extraction errors typical in an industrial environment. It also proposes an automated solution to extraction error diagnosis and correction. Experiments on circuits with similar architecture to that of high speed custom-made industrial blocks are conducted to confirm the fitness of the approach. 1