Testability considerations for implementing an embedded memory subsystem

textThere are a number of testability considerations for VLSI design, but test coverage, test time, accuracy of test patterns and correctness of design information for DFD (Design for debug) are the most important ones in design with embedded memories. The goal of DFT (Design-for-Test) is to achieve zero defects. When it comes to the memory subsystem in SOCs (system on chips), many flavors of memory BIST (built-in self test) are able to get high test coverage in a memory, but often, no proper attention is given to the memory interface logic (shadow logic). Functional testing and BIST are the most prevalent tests for this logic, but functional testing is impractical for complicated SOC designs. As a result, industry has widely used at-speed scan testing to detect delay induced defects. Compared with functional testing, scan-based testing for delay faults reduces overall pattern generation complexity and cost by enhancing both controllability and observability of flip-flops. However, without proper modeling of memory, Xs are generated from memories. Also, when the design has chip compression logic, the number of ATPG patterns is increased significantly due to Xs from memories. In this dissertation, a register based testing method and X prevention logic are presented to tackle these problems. An important design stage for scan based testing with memory subsystems is the step to create a gate level model and verify with this model. The flow needs to provide a robust ATPG netlist model. Most industry standard CAD tools used to analyze fault coverage and generate test vectors require gate level models. However, custom embedded memories are typically designed using a transistor-level flow, there is a need for an abstraction step to generate the gate models, which must be equivalent to the actual design (transistor level). The contribution of the research is a framework to verify that the gate level representation of custom designs is equivalent to the transistor-level design. Compared to basic stuck-at fault testing, the number of patterns for at-speed testing is much larger than for basic stuck-at fault testing. So reducing test and data volume are important. In this desertion, a new scan reordering method is introduced to reduce test data with an optimal routing solution. With in depth understanding of embedded memories and flows developed during the study of custom memory DFT, a custom embedded memory Bit Mapping method using a symbolic simulator is presented in the last chapter to achieve high yield for memories.Electrical and Computer Engineerin

High level behavioural modelling of boundary scan architecture.

This project involves the development of a software tool which enables the integration of the IEEE 1149.1/JTAG Boundary Scan Test Architecture automatically into an ASIC (Application Specific Integrated Circuit) design. The tool requires the original design (the ASIC) to be described in VHDL-IEEE 1076 Hardware Description Language. The tool consists of the two major elements: i) A parsing and insertion algorithm developed and implemented in 'C'; ii) A high level model of the Boundary Scan Test Architecture implemented in 'VHDL'. The parsing and insertion algorithm is developed to deal with identifying the design Input/Output (I/O) terminals, their types and the order they appear in the ASIC design. It then attaches suitable Boundary Scan Cells to each I/O, except power and ground and inserts the high level models of the full Boundary Scan Architecture into the ASIC without altering the design core structure

FPGA ARCHITECTURE AND VERIFICATION OF BUILT IN SELF-TEST (BIST) FOR 32-BIT ADDER/SUBTRACTER USING DE0-NANO FPGA AND ANALOG DISCOVERY 2 HARDWARE

The integrated circuit (IC) is an integral part of everyday modern technology, and its application is very attractive to hardware and software design engineers because of its versatility, integration, power consumption, cost, and board area reduction. IC is available in various types such as Field Programming Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), System on Chip (SoC) architecture, Digital Signal Processing (DSP), microcontrollers (μC), and many more. With technology demand focused on faster, low power consumption, efficient IC application, design engineers are facing tremendous challenges in developing and testing integrated circuits that guaranty functionality, high fault coverage, and reliability as the transistor technology is shrinking to the point where manufacturing defects of ICs are affecting yield which associates with the increased cost of the part. The competitive IC market is pressuring manufactures of ICs to develop and market IC in a relatively quick turnaround which in return requires design and verification engineers to develop an integrated self-test structure that would ensure fault-free and the quality product is delivered on the market. 70-80% of IC design is spent on verification and testing to ensure high quality and reliability for the enduser. To test complex and sophisticated IC designs, the verification engineers must produce laborious and costly test fixtures which affect the cost of the part on the competitive market. To avoid increasing the part cost due to yield and test time to the end-user and to keep up with the competitive market many IC design engineers are deviating from complex external test fixture approach and are focusing on integrating Built-in Self-Test (BIST) or Design for Test (DFT) techniques onto IC’s which would reduce time to market but still guarantee high coverage for the product. Understanding the BIST, the architecture, as well as the application of IC, must be understood before developing IC. The architecture of FPGA is elaborated in this paper followed by several BIST techniques and applications of those BIST relative to FPGA, SoC, analog to digital (ADC), or digital to analog converters (DAC) that are integrated on IC. Paper is concluded with verification of BIST for the 32-bit adder/subtracter designed in Quartus II software using the Analog Discovery 2 module as stimulus and DE0-NANO FPGA board for verification

Innovative Techniques for Testing and Diagnosing SoCs

We rely upon the continued functioning of many electronic devices for our everyday welfare, usually embedding integrated circuits that are becoming even cheaper and smaller with improved features. Nowadays, microelectronics can integrate a working computer with CPU, memories, and even GPUs on a single die, namely System-On-Chip (SoC). SoCs are also employed on automotive safety-critical applications, but need to be tested thoroughly to comply with reliability standards, in particular the ISO26262 functional safety for road vehicles. The goal of this PhD. thesis is to improve SoC reliability by proposing innovative techniques for testing and diagnosing its internal modules: CPUs, memories, peripherals, and GPUs. The proposed approaches in the sequence appearing in this thesis are described as follows: 1. Embedded Memory Diagnosis: Memories are dense and complex circuits which are susceptible to design and manufacturing errors. Hence, it is important to understand the fault occurrence in the memory array. In practice, the logical and physical array representation differs due to an optimized design which adds enhancements to the device, namely scrambling. This part proposes an accurate memory diagnosis by showing the efforts of a software tool able to analyze test results, unscramble the memory array, map failing syndromes to cell locations, elaborate cumulative analysis, and elaborate a final fault model hypothesis. Several SRAM memory failing syndromes were analyzed as case studies gathered on an industrial automotive 32-bit SoC developed by STMicroelectronics. The tool displayed defects virtually, and results were confirmed by real photos taken from a microscope. 2. Functional Test Pattern Generation: The key for a successful test is the pattern applied to the device. They can be structural or functional; the former usually benefits from embedded test modules targeting manufacturing errors and is only effective before shipping the component to the client. The latter, on the other hand, can be applied during mission minimally impacting on performance but is penalized due to high generation time. However, functional test patterns may benefit for having different goals in functional mission mode. Part III of this PhD thesis proposes three different functional test pattern generation methods for CPU cores embedded in SoCs, targeting different test purposes, described as follows: a. Functional Stress Patterns: Are suitable for optimizing functional stress during I Operational-life Tests and Burn-in Screening for an optimal device reliability characterization b. Functional Power Hungry Patterns: Are suitable for determining functional peak power for strictly limiting the power of structural patterns during manufacturing tests, thus reducing premature device over-kill while delivering high test coverage c. Software-Based Self-Test Patterns: Combines the potentiality of structural patterns with functional ones, allowing its execution periodically during mission. In addition, an external hardware communicating with a devised SBST was proposed. It helps increasing in 3% the fault coverage by testing critical Hardly Functionally Testable Faults not covered by conventional SBST patterns. An automatic functional test pattern generation exploiting an evolutionary algorithm maximizing metrics related to stress, power, and fault coverage was employed in the above-mentioned approaches to quickly generate the desired patterns. The approaches were evaluated on two industrial cases developed by STMicroelectronics; 8051-based and a 32-bit Power Architecture SoCs. Results show that generation time was reduced upto 75% in comparison to older methodologies while increasing significantly the desired metrics. 3. Fault Injection in GPGPU: Fault injection mechanisms in semiconductor devices are suitable for generating structural patterns, testing and activating mitigation techniques, and validating robust hardware and software applications. GPGPUs are known for fast parallel computation used in high performance computing and advanced driver assistance where reliability is the key point. Moreover, GPGPU manufacturers do not provide design description code due to content secrecy. Therefore, commercial fault injectors using the GPGPU model is unfeasible, making radiation tests the only resource available, but are costly. In the last part of this thesis, we propose a software implemented fault injector able to inject bit-flip in memory elements of a real GPGPU. It exploits a software debugger tool and combines the C-CUDA grammar to wisely determine fault spots and apply bit-flip operations in program variables. The goal is to validate robust parallel algorithms by studying fault propagation or activating redundancy mechanisms they possibly embed. The effectiveness of the tool was evaluated on two robust applications: redundant parallel matrix multiplication and floating point Fast Fourier Transform

Fault-Tolerant Computing: An Overview

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNASA / NAG-1-613Semiconductor Research Corporation / 90-DP-109Joint Services Electronics Program / N00014-90-J-127

Design and Validation of Network-on-Chip Architectures for the Next Generation of Multi-synchronous, Reliable, and Reconfigurable Embedded Systems

NETWORK-ON-CHIP (NoC) design is today at a crossroad. On one hand, the design principles to efficiently implement interconnection networks in the resource-constrained on-chip setting have stabilized. On the other hand, the requirements on embedded system design are far from stabilizing. Embedded systems are composed by assembling together heterogeneous components featuring differentiated operating speeds and ad-hoc counter measures must be adopted to bridge frequency domains. Moreover, an unmistakable trend toward enhanced reconfigurability is clearly underway due to the increasing complexity of applications. At the same time, the technology effect is manyfold since it provides unprecedented levels of system integration but it also brings new severe constraints to the forefront: power budget restrictions, overheating concerns, circuit delay and power variability, permanent fault, increased probability of transient faults. Supporting different degrees of reconfigurability and flexibility in the parallel hardware platform cannot be however achieved with the incremental evolution of current design techniques, but requires a disruptive approach and a major increase in complexity. In addition, new reliability challenges cannot be solved by using traditional fault tolerance techniques alone but the reliability approach must be also part of the overall reconfiguration methodology. In this thesis we take on the challenge of engineering a NoC architectures for the next generation systems and we provide design methods able to overcome the conventional way of implementing multi-synchronous, reliable and reconfigurable NoC. Our analysis is not only limited to research novel approaches to the specific challenges of the NoC architecture but we also co-design the solutions in a single integrated framework. Interdependencies between different NoC features are detected ahead of time and we finally avoid the engineering of highly optimized solutions to specific problems that however coexist inefficiently together in the final NoC architecture. To conclude, a silicon implementation by means of a testchip tape-out and a prototype on a FPGA board validate the feasibility and effectivenes

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations