As the industry advances towards smaller geometries for integrated circuits (ICs), internal cell defects have become increasingly crucial to address. Cell-Aware Testing (CAT) has emerged as the industry standard for achieving the high-quality levels required in modern ICs, as it explicitly targets intra-cell defects. However, the comprehensive defect coverage provided by CAT can result in a high test pattern count, which directly impacts test time and production costs.This paper proposes two complementary methodologies to optimize the cell library characterization phase by leveraging weak detections to maximize the number of Don’t Cares (DCs) and equivalent defects. The proposal produces an optimized Defect Detection Matrix (DDM) for the library, which is then used by the ATPG for pattern generation at the design level.The methodologies have been validated by running CAT ATPG for five different designs, using a 65nm cell library characterized with the proposed approach. The experimental results show that using the optimized DDMs reduces the number of patterns by an average of 8% and the pattern generation time by 11%, depending on the methodology used and the circuit analyzed.

Traditional structural tests are powerful automatic approaches for capturing faulty behavior in integrated circuits. Besides the ease of generating test patterns, structural methods are known to be able to cover a vast but incomplete spectrum of all possible faults in a System-on-Chip (SoC). A new step in the manufacturing test flow has been added to fill the leftover gaps of structural tests, called the System-Level Test (SLT), which resembles the final workload, and environment.This work illustrates how to build up an automated generation engine to synthesize SLT programs that effectively attack structural test weaknesses from both a holistic and an analytical perspective. The methodology targets the crossbar module, as one of the most critical areas in the SoC, and it simultaneously creates a ripple effect across the un-core logic.Experimental results are conducted on an automotive SoC manufactured by STMicroelectronics.

This paper explores the trade-offs of reducing scan test patterns during Wafer Sort, accepting additional packaging costs, and screening more chips during Package Tests. Previous works proposed ways of selecting or reordering patterns to bring the most efficient to the left. Unlike such studies, this work quantifies the benefit of removing patterns directly from the tail of any pattern set. The paper elaborates on novel formulas to propose a comprehensive cost model that combines yield, Wafer Sort, packaging, and Package Test costs. The model evolves from known concepts by assuming that mass production defectivity is non-uniformly distributed over the die population and accounts for sacrificial lots to extract guiding information. It is shown that reducing patterns at Wafer Sort is beneficial under certain conditions of yield, fault coverage, and considering equipment and production costs. The model accurately estimates the number of patterns to remove for maximum gain in these cases. As a further by-product, the paper shows that a significant cost advantage can be achieved if pattern generation is guided based on the basics of the non-uniform failure distribution. This approach is validated with an academic benchmark and by observing six months of production for a real-world microcontroller by STMicroelectronics.

Electronics play a significant role in modern society in various areas of our daily lives. Companies producing embedded nano-electronic systems have responded to the ever-increasing demand for high-performance chips with the development and production of structurally complex design, both in terms of the number of gates they are composed of and how they are arranged on the silicon surface. Especially devices intended for safety-critical fields, such as the Automotive field, require a thorough and precise testing process before they are fielded. This paper proposes a correlation analysis between candidate faulty logical gates as possible sources of a given failure identified during the Manufacturing Test Flow and their layout characteristics on the silicon. It is meaningful feedback for manufacturers about the quality of their applied tests. The experimental results are reported for data regarding a production lot of an Automotive System-on-Chip belonging to the SPC58 family produced by STMicroelectronics.

This paper encompasses three contributions by industry professionals and university researchers. The contributions describe different trends in automotive products, including both manufacturing test and run-time reliability strategies. The subjects considered in this session deal with critical factors, from optimizing the final test before shipment to market to in-field reliability during operative life.

Embedded nano-electronic systems are becoming more prevalent in people's daily lives. As a result, chip and embedded system manufacturing has become increasingly complicated and huge in recent years. Considering safety-critical sectors, such as automotive, it is evident how managing system anomalies and defects becomes vital. Thus, it is necessary to develop and investigate innovative methodologies that can guarantee high reliability despite modern Systems-on-Chip's complexity in critical safety fields. Significant attempts were made to market incredibly reliable microelectronic components. In order to ensure the reliability of the devices, the Automotive field has also started focusing on collecting large amounts of data from car fleets. The data are collected in-field during the life cycle of the devices and create effective feedback for designers and manufacturers. This paper proposes a methodology to store and collect data from key-on and key-off tests performed by Logic BIST for an industrial case study produced by STMicroelectronics.