Reducing Library Characterization Time for Cell-aware Test while Maintaining Test Quality

Cell-aware test (CAT) explicitly targets faults caused by defects inside library cells to improve test quality, compared with conventional automatic test pattern generation (ATPG) approaches, which target faults only at the boundaries of library cells. The CAT methodology consists of two stages. Stage 1, based on dedicated analog simulation, library characterization per cell identifies which cell-level test pattern detects which cell-internal defect; this detection information is encoded in a defect detection matrix (DDM). In Stage 2, with the DDMs as inputs, cell-aware ATPG generates chip-level test patterns per circuit design that is build up of interconnected instances of library cells. This paper focuses on Stage 1, library characterization, as both test quality and cost are determined by the set of cell-internal defects identified and simulated in the CAT tool flow. With the aim to achieve the best test quality, we first propose an approach to identify a comprehensive set, referred to as full set, of potential open- and short-defect locations based on cell layout. However, the full set of defects can be large even for a single cell, making the time cost of the defect simulation in Stage 1 unaffordable. Subsequently, to reduce the simulation time, we collapse the full set to a compact set of defects which serves as input of the defect simulation. The full set is stored for the diagnosis and failure analysis. With inspecting the simulation results, we propose a method to verify the test quality based on the compact set of defects and, if necessary, to compensate the test quality to the same level as that based on the full set of defects. For 351 combinational library cells in Cadence’s GPDK045 45nm library, we simulate only 5.4% defects from the full set to achieve the same test quality based on the full set of defects. In total, the simulation time, via linear extrapolation per cell, would be reduced by 96.4% compared with the time based on the full set of defects

Speeding up Cell-Aware Library Characterization by Preceding Simulation with Structural Analysis

Cell-aware test (CAT) offers a high-quality test that explicitly targets potential cell-internal open and short defects. CAT requires to characterize library cells to determine which cell patterns can detect which cell-internal defects. Characterization is performed only once per library, but in today's implementations [1], [2] it is nevertheless a very time-consuming task, since it performs analog simulation on every library cell c, for every potential defect d, and with every cell pattern p. However, after the lengthy simulation, invariably a majority of the defect/pattern tuples (d, p) turns out to be undetectable. Often, an undetectable tuple (d, p) can be identified only on the basis of the topology of the cell's transistors netlist. We refer to this as logical undetectability. This paper presents an efficient algorithm that performs a structural analysis of library cells to identify all logically undetectable tuples (d, p), which then can be excluded from the time-consuming analog simulation. As our structural analysis is a lot faster than the analog simulation, their combination delivers significant speed-ups; for 476 standard cells from Cadence's GPDK045 library [3], the algorithm identified 47% of logically undetectable tuples

Defect-location identification for cell-aware test

Cell-aware test (CAT) explicitly targets defects inside library cells and therefore significantly reduces the amount of test escapes compared to conventional automatic test pattern generation (ATPG). Our CAT flow consists of three steps: (1) defect-location identification (DLI), (2) defect characterization based on detailed analog simulation of the cells, and (3) cell-aware automatic test pattern generation (ATPG). This paper focuses on Step 1, as quality and cost are determined by the set of cell-internal defect locations considered in the remainder of the flow. Based on technology inputs from the user and a parasitic extraction (PEX) run that analyzes the cell layouts, we derive a set of open defects on and short defects between both transistor terminals and intra-cell interconnects. The full set of defect locations is stored for later use during failure analysis. Through dedicated DLI algorithms, we identify a compact subset of defect locations for defect characterization and ATPG, in which we include only one representative defect location for each set of equivalent defects locations. For Cadence’s GPDK045 library, the compact subset contains only 2.8% of the full set of defect locations and reduces the time required for defect characterization with the same ratio

Defect-location identification for cell-aware test

Cell-aware test (CAT) explicitly targets defects inside library cells and therefore significantly reduces the amount of test escapes compared to conventional automatic test pattern generation (ATPG). Our CAT flow consists of three steps: (1) defect-location identification (DLI), (2) defect characterization based on\u3cbr/\u3edetailed analog simulation of the cells, and (3) cell-aware automatic test pattern generation (ATPG). This paper focuses on Step 1, as quality and cost are determined by the set of cell-internal defect locations considered in the remainder of the flow. Based on technology inputs from the user and a parasitic extraction (PEX) run that analyzes the cell layouts, we derive a set of open defects on and short defects between both transistor terminals and intra-cell interconnects. The full set of defect locations is stored for later use during failure analysis. Through dedicated DLI algorithms, we identify a compact subset of defect locations for defect characterization and ATPG, in which we include only\u3cbr/\u3eone representative defect location for each set of equivalent defects locations. For Cadence’s GPDK045 library, the compact subset contains only 2.8% of the full set of defect locations and reduces the time required for defect characterization with the same ratio

Tightening the Mesh Size of the Cell-Aware ATPG Net for Catching All Detectable Weakest Faults

Cell-aware test (CAT) explicitly targets faults caused by cell-internal short and open defects and has been shown to significantly reduce test escape rates. CAT library cell characterization is typically done for only two defect resistance values: one representing hard opens and another one representing hard shorts. In this paper, similar to fishermen tightening the mesh size of their nets to catch small fish, we perform library characterization as efficiently as possible for a set of resistances representing increasingly weaker defects, and then adjust our ATPG flow to explicitly target faults caused by the weakest still-detectable variant of each potential defect. We implemented this novel approach in an experimental ATPG tool flow script, using functions of Cadence's Modus as building blocks. To assess the effectiveness of our approach, we formulate a new dedicated test metric: the weakest fault coverage wfc. Compared to conventional CAT targeting hard defects only, experimental results show that our new approach enhances detection of weakest faults and significantly reduces wfc escapes =1-wfc, while maintaining its original (hard-defect) fault coverage fc, of course at the expense of (acceptable) increases in the required number of test patterns and associated test generation time

Optimization of cell-aware ATPG results by manipulating library cells' defect detection matrices

\u3cp\u3eCell-aware test (CAT) explicitly targets defects inside library cells and therefore significantly reduces the number of test escapes compared to conventional automatic test pattern generation (ATPG) approaches that cover cell-internal defects only serendipitously. CAT consists of two steps, viz. (1) library characterization and (2) cell-aware ATPG. Defect detection matrices (DDMs) are used as the interface between both CAT steps; they record which cell-internal defects are detected by which cell-level test patterns. This paper proposes two algorithms that manipulate DDMs to optimize cell-aware ATPG results with respect to fault coverage, test pattern count, and compute time. Algorithm 1 identifies don't-care bits in cell patterns, such that the ATPG tool can exploit these during cell-to-chip expansion to increase fault coverage and reduce test-pattern count. Algorithm 2 selects, at cell level, a subset of preferential patterns that jointly provides maximal fault coverage at a minimized stimulus care-bit sum. To keep the ATPG compute time under control, we run cell-aware ATPG with the preferential patterns first, and a second ATPG run with the remaining patterns only if necessary. Selecting the preferential patterns maps onto a well-known N Phard problem, for which we derive an innovative heuristic that outperforms solutions in the literature. Experimental results on twelve circuits show average reductions of 43% of non-covered faults and 10% in chip-pattern count.\u3c/p\u3

Application of cell-aware test on an advanced 3nm CMOS technology library

Advanced technology nodes employ a large number of innovations. In addition, they require 'scaling boosters' in the design of standard-cell libraries to be able to offer the scaling benefits in area, performance, and power that we have grown accustomed to. Consequently, sub-10nm standard cells are significantly more complex than their predecessors. Cell-aware test (CAT) explicitly targets cell-internal resistive open and short defects identified through extensive characterization of the library cells. This paper is (to the best of our knowledge) the first to report on the application of CAT library characterization on a sub-10nm technology node. We used Cadence's CAT tool flow on an experimental 114-cell-library in IMEC's 3nm CMOS technology iN5. Despite the increased cell complexity, we show that the CAT flow still works, and that compared with functionally-comparable library cells in a 45nm technology, the number of potential non-equivalent defect locations, cell-level test patterns, and defect coverage did not change drastically

Application of cell-aware test on an advanced 3nm CMOS technology library

\u3cp\u3eAdvanced technology nodes employ a large number of innovations. In addition, they require 'scaling boosters' in the design of standard-cell libraries to be able to offer the scaling benefits in area, performance, and power that we have grown accustomed to. Consequently, sub-10nm standard cells are significantly more complex than their predecessors. Cell-aware test (CAT) explicitly targets cell-internal resistive open and short defects identified through extensive characterization of the library cells. This paper is (to the best of our knowledge) the first to report on the application of CAT library characterization on a sub-10nm technology node. We used Cadence's CAT tool flow on an experimental 114-cell-library in IMEC's 3nm CMOS technology iN5. Despite the increased cell complexity, we show that the CAT flow still works, and that compared with functionally-comparable library cells in a 45nm technology, the number of potential non-equivalent defect locations, cell-level test patterns, and defect coverage did not change drastically.\u3c/p\u3