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.

Developing functional test programs for hardware testing is time-consuming and experience-wise. A functional test program’s quality is usually assessed only through expensive fault simulation campaigns during early development. This paper presents indirect quality measurements of fault detection capabilities of functional test programs to reduce the total cost of fault simulation in the early development stages. We present a methodology that analyzes the instruction trace generated by running functional test programs on-chip and building its control and dataflow graph. We use the graph to identify potential flaws that affect the program’s fault detection capabilities. We present different graph-based techniques to measure the programs’ quality indirectly. By exploiting standard debugging formats, we individuate instructions in the source code that affect the graph-based measurements. We perform experiments on an automotive device manufactured by STMicroelectronics, running functional test programs of different natures. Our results show that our metric allows test engineers to develop better functional test programs without basing their development solely on fault simulation campaigns.

This paper deals with functional System-Level Test (SLT) for System-on-Chips (SoCs) communication peripherals. The proposed methodology is based on analyzing the potential weaknesses of applied structural tests such as Scan-based. Then, the paper illustrates how to develop a functional SLT programs software suite to address such issues. In case the communication peripheral provides detection/correction features, the methodology proposes the design of a hardware companion module to be added to the Automatic Test Equipment (ATE) to interact with the SoC communication module by purposely corrupting data frames. Experimental results are obtained on an industrial, automotive SoC produced by STMicroelectronics focusing on the Controller Area Network (CAN) communication peripheral and showing the effectiveness of the SLT suite to complement structural tests.

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.

Embedded nano-electronic devices have spread in daily life over the past ten years. Chip and embedded system manufacturing has thus become more challenging in recent years.When safety-critical sectors like the automobile are considered, addressing system anomalies and faults is crucial. Therefore, it is necessary to develop and research innovative ways to maintain high reliability in safety-critical sectors despite the complexity of present Systems-on-Chip (SoCs).In order to ensure high reliability, and be compliant with reliability standards, designers started to add additional circuitry to perform on-device tests. Built-In-Self-Test (BIST) is a technology that allows to conduct exhaustive tests within devices and, most importantly, without the need for external equipment. BIST can detect faults by outputting a signature at test end, which can be compared with a known value. Thus such known signatures are key, and in case of a signature mismatch it is not trivial to understand the root cause of the failure.This paper proposes a methodology to find the first failing pattern which causes the BIST’s signature to deviate and a way to collect good signatures from in-field devices, at key on/off, where BISTs are programmed and executed by the firmware at maximum frequency for an industrial case study produced by STMicroelectronics.The transition delay fault model is the primary target for the described work.

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.

Functional test programs are increasingly used as flexible approaches to verify device functionality, both online (e.g., Software-Based Self Tests encapsulated in Software Test Library) and during the manufacturing test flow (e.g., System-Level Test). However, a traditional integrated development environment seldom analyzes functional test program weaknesses regarding fault-masking and propagation in CPU registers. Therefore, understanding the presence of errors in the programs due to logic faults at the end of the test program is only assessed by a signature computation, e.g., xor operation between all registers. The presence of program weaknesses in terms of data or control flow is not assessed, and it may lead to fault escapes and/or masking. In particular, those are perfect conditions for proliferating Silent data corruption and unrecoverable errors at the system level.This work aims to introduce a guided debugger-based fault injector framework to verify the fault propagation and masking capabilities of functional test programs by eventually catching program errors without relying on time-consuming simulation-based approaches. The fault-free instruction trace is dumped and analyzed to provide information about possible target registers of specific instructions for injecting faults, e.g., in jump instructions, where errors into registers may change the execution flow or not, to verify the presence of silent data corruptions and errors.The experimental results are carried out on an automotive device from the SPC58 family manufactured by STMicroelectronics, and faults are injected through a script running on Power Debug E40 from Lauterbach.

Burn-In test equipment usually owns extensive memory capabilities to store pre-computed patterns to be applied to the circuit inputs as well as ad-hoc circuitries to drive and read the DUT pins during the BI phase. The solution proposed in this paper dramatically reduces the memory size requirement and just demands a generic microcontroller unit (MCU) equipped with a couple of embedded processors, some standard common peripheral units, and a few KB memories. Moreover, the proposed Burn-In tester could be integrated into a System Level Test equipment which is typically based on MCUs to communicate functionally with the DUT. This paper provides full details about the architecture of such a low-cost innovative tester, which can supply the DUT with unlimited pseudo-random patterns created autonomously by the MCU firmware from any selected seed. The tester prototype developed to collect experimental results includes a low-cost System-on-Chip based on a multi-core MCU and a set of peripheral cores, encompassing timers and Direct Memory Access modules. The tester prototype is used to stress an automotive chip accounting for about 20 million gates, 700 thousand scan flip-flops, and several scan modes. The combination of pseudo-random pattern generation with the ability to control different scan and Design for Testability (DfT) modes, including LBIST, permits to reach a higher coverage of stress metrics than by the application of a limited set of pre-computed ATPG patterns. The toggle coverage level reached is up to 95.89%. The application speed achieved by the tester with non-optimized connections is up to about 10MHz.

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.

The complexity of automotive Systems-on-a-Chip (SoCs) has enormously grown in the last decades. Today’s automotive SoCs are compelling due to technology improvements, different integration technologies, increased heterogeneity, and many available embedded memories. On balance, despite testing techniques that have been refined through years, traditional structural test methods, like scan and BIST, can cover a vast but not complete spectrum of all the possible defects. It appears that the divide-and-conquer approach founded on structural techniques may not be enough to reach every single element or to effectively stimulate the faulty behaviors that may show up during the lifetime of the device. Burn-In is widely used to reduce Infant Mortality, accelerating the evolution of weak points into defects via externally or internally induced stress.In this work, we focus on internal stress and present a generation strategy intended to automatically produce functional stress procedures for the Burn-In phase that exacerbate possible weak points which are likely to escape activation by structural tests, such that they more easily outbreak during the successive final test procedures. The proposed generation strategy primarily addresses the interconnections to embedded memories, which look challenging to stress by structural methods, including Logic and Memory BIST, and critical due to the integration of different technologies (i.e., logic gates and memory layout). In the considered test case, the proposed approach increases the average toggle activity by orders of magnitude with respect to Memory BIST. Furthermore, it provides a uniform distributed toggling activity.Results collected on an automotive SoC show how the stress provided by functional programs compares with the stress level provided by structural test methods measured in terms of toggling activity. The SpeedUp produced by the proposed procedure is 3.14X wrt to the MBIST executing the March C-algorithm.

Testing and validation check a hardware device or a software application against the desired design requirements. They are a vital part of all steps of system engineering and typically account for a significant percentage of the overall development cost. This paper presents a novel technique to provide a quick preliminary evaluation of functional test procedures of various natures, ranging from Software-Based Self-Test to Burn-In Functional Stress and System-level tests. We define a new metric called “connectivity”, which is fast to compute and can be used to guide functional program development. The method does not require logic or fault simulations, and it is based on the analysis of the execution trace generated by the functional program. To summarize our process, we first obtain the trace directly from the chip, running the software through a debugger. Then, we create a graph representation of the program data flow. Finally, we analyze the graph to identify instructions that negatively impact the final coverage. We perform experiments on an automotive device manufactured by STMicroelectronics, and we demonstrate the effectiveness of the approach in terms of computation time and beneficial effects on the fault coverage.

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.

With the explosion of off-the-shelf SoCs in terms of size and the advent of novel techniques related to failure modes, commercial ATPG and fault simulation engines can often be insufficient to measure the coverage of very specific metrics. In these cases, many researchers firstly store the simulation trace during the analysis phase. Then, they collect the desired statistics during a post-processing step. In this framework, the so-called Value Change Dump (VCD) is a very commonly used file format to record simulation traces. The target of this paper is twofold. From the one hand, we illustrate some Burn-In (BI) related metrics which cannot be evaluated by current commercial fault simulators and ATPG engines. These metrics are indeed based on a post-processing analysis of memory dumps in VCD format. From the other hand, we mitigate the evaluation time and the memory required to analyze huge VCD files by exploiting optimization techniques coming from modern programming features and smart parallelization. Adopting this strategy, we can analyze simulation dumps of more than 250 GBytes in less than one hour, showing improvements of two orders of magnitude over previous tools, with a consequent higher scalability and testability power.

Automotive systems must reach a high reliability in their electronic components. This kind of devices must undergo several tests and stress steps discovering all possible defects that could manifest during lifetime. Burn-In (BI) is a manufacturing test phase used for screening the early life latent faults that can naturally affect a population of devices. System Level Test (SLT) is increasingly adopted as one of the final steps in the testing process of complex Systems on Chip (SoCs) mimicking the operational conditions. This paper aims at describing the motivations for and the effectiveness stemming from combining SLT with BI. The key idea leverages on the development of a new step inside the test process, which reproduces the system using SLT and places the system in the worst cases by means of the BI. Moreover, the paper analyses the required tester architecture to merge SLT and BI. Finally, an industrial case by STMicroelectronics is used to demonstrate the possible cost reduction.

This paper describes an optimized embedded memory diagnosis flow that exploits many levels of knowledge to produce accurate failure hypothesis. The proposed post-processing analysis flow is composed of many steps investigating failure shapes as well as cell fail syndromes, and includes advanced techniques to tackle incomplete data possibly due to tester noise and/or by faults showing intermittent effects. The effectiveness of the technique is demonstrated on an automotive-oriented System-on-Chip (SoC) manufactured in a 90nm technology by STMicroelectronics, which includes embedded SRAM memory cores tested using a programmable BIST. Scrambled BITMAPS gives a visual feedback leading to quick physical defect identification. Such research is relevant to aid on the manufacturing, material and process enhancements raising silicon yield.

Embedded memory modules are sensitive components that deeply influence production yield of integrated devices. For fast yield improvement, an efficient manufacturing test must supply advanced defect characterization that helps in discovering technology weaknesses and finding strategies for improvement. This paper presents an industrial workflow for embedded memory diagnosis. It is based on the integration of March-based diagnostic BIST hardware in an IEEE 1500-compliant environment, and on a novel diagnostic algorithm for determining the fault model associated to the retrieved syndromes. An experimental implementation showing the feasibility of the approach is presented

Modern systems-on-chip (SoCs) allow integrating many different functional cores in the same piece of silicon. Their test requires taking fast decisions in the selection of structures and strategies at different stages of the design flow: early computation of area overhead, power consumption and test application time are indispensable in order to develop effective and efficient test for the overall chip, while taking into account physical constraints imposed by the available test equipment. Furthermore, once the test strategy has been selected and patterns generated for each module, additional nonnegligible effort is required to integrate the test program in an ATE-readable format. In this paper, we tackle these problems by means of a new software platform, leveraging descriptions of both the core-level test structure and the system-level requirements. Experimental results related to a realistic case of study underline the effectiveness of the tool and its potentialities in the IEEE 1500 environments

Complex SOC's developed in VDSM technologies require adequate solutions to diagnose and analyze yield losses. This paper focuses on the diagnosis of logic circuits embedded in SOCs. The core instrument leveraged is ATPG used during test vectors generation and analysis of failures. This work emphasizes the results obtained in systematically applying ATPG diagnosis on failures detected in the manufacturing test floor. Details on diagnosis flow and ATE data collection are given. Experimental results are provided.

This paper addresses the issue of testing and diagnosing a memory core embedded in a complex SOC. The proposed solution is based on a P1500-compliant wrapper that follows a programmable BIST approach and is able to support both testing and diagnosis. Experimental results are provided allowing to evaluate the benefits and limitations of the adopted solution and to compare it with previously proposed ones. The solution takes into account several constraints existing in an industrial environment, such as minimizing the cost of test development, easing the reuse of the available architectures for test and diagnosis of different memory types and minimizing the cost of the external ATE.