Artificial Intelligence hardware accelerators are pervading the chip market. Most of the time, they are third-party IPs integrated by silicon manufacturers. As a consequence, their design may be obfuscated, which can introduce issues from a manufacturing testing perspective. This paper illustrates how to effectively and efficiently select the most appropriate functional stress stimuli for Artificial Intelligence (AI) Hardware (HW) Accelerators embedded in System-on-Chip (SoC). The proposed methodology is netlist independent; and it is based on both current measurements from the real chip and architectural evaluations. These ingredients are heuristically used to rank and sift the optimal functional patterns to apply along the Burn-In (BI) phase. Experimental results on two different Automotive SoCs manufactured by STMicroelectronics, demonstrate the effectiveness and efficiency of the proposed method.

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.

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.