A machine learning-based approach to optimize repair and increase yield of embedded flash memories in automotive systems-on-chip

Nowadays, Embedded Flash Memory cores occupy a significant portion of Automotive Systems-on-Chip area, therefore strongly contributing to the final yield of the devices. Redundancy strategies play a key role in this context; in case of memory failures, a set of spare word- and bit-lines are allocated by a replacement algorithm that complements the memory testing procedure. In this work, we show that replacement algorithms, which are heavily constrained in terms of execution time, may be slightly inaccurate and lead to classify a repairable memory core as unrepairable. We denote this situation as Flash memory false fail. The proposed approach aims at identifying false fails by using a Machine Learning approach that exploits a feature extraction strategy based on shape recognition. Experimental results carried out on the manufacturing data show a high capability of predicting false fails

머신 러닝 기반의 낸드 플래시 칩 eFuse 구성 생성 자동화 방법론

학위논문(석사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2019. 8. 유승주.Post fabrication process is becoming more and more important as memory technology becomes complex, in the bid to satisfy target performance and yield across diverse business domains, such as servers, PCs, automotive, mobiles, and embedded devices, etc. Electronic fuse adjustment (eFuse optimization and trimming) is a traditional method used in the post fabrication processing of memory chips. Engineers adjust eFuse to compensate for wafer inter-chip variations or guarantee the operating characteristics, such as reliability, latency, power consumption, and I/O bandwidth. These require highly skilled expert engineers and yet take significant time. This paper proposes a novel machine learning-based method of automatic eFuse configuration to meet the target NAND flash operating characteristics. The proposed techniques can maximally reduce the expert engineers workload. The techniques consist of two steps: initial eFuse generation and eFuse optimization. In the first step, we apply the variational autoencoder (VAE) method to generate an initial eFuse configuration that will probably satisfy the target characteristics. In the second step, we apply the genetic algorithm (GA), which attempts to improve the initial eFuse configuration and finally achieve the target operating characteristics. We evaluate the proposed techniques with Samsung 64-Stacked vertical NAND (VNAND) in mass production. The automatic eFuse configuration takes only two days to complete the implementation.메모리 공정 기술이 발전하고 비즈니스 시장이 다양해 짐에 따라 웨이퍼 수율을 높이고 비즈니스 특성 목표를 만족하기 위한 후 공정 과정이 매우 중요해 지고 있다. 전기적 퓨즈 조절 방식(이-퓨즈 최적화 및 트림)은 메모리 칩 후 공정 과정에서 사용되는 전통적인 방식이다. 엔지니어는 이-퓨즈 조절을 통해 웨이퍼 상의 칩들 간의 초기 특성의 변화를 보상하거나, 신뢰성, 레이턴시, 파워 소모, 그리고 I/O 대역폭 등의 칩 목표 특성을 보장한다. 이-퓨즈 조절 업무는 다수의 숙련된 엔지니어가 필요하고 또한 상당히 많은 시간을 소모한다. 본 논문에서는 낸드 플래시 칩의 동작 특성 목표를 얻기 위한 기계 학습 기반의 이-퓨즈 자동 생성 기술을 제안하고, 해당 기술은 엔지니어의 작업시간을 획기적으로 단축시킬 수 있다. 논문의 기술은 두 단계로 구성 된다. 첫 번째 단계에서는 variational autoencoder (VAE) 기술을 적용하여 목표하는 동작 특성을 만족시키는 초기 이-퓨즈 구성을 생성한다. 두 번째 단계에서는 유전 알고리즘을 적용하여 초기 생성된 이-퓨즈 구성에 대하여 목표하는 성능 특성과의 정합성을 추가로 개선하여 최종적으로 목표하는 성능 특성을 얻는다. 논문의 평가는 실제 양산중인 삼성 64단 브이낸드 제품을 이용하여 진행하였다. 논문의 이-퓨즈 자동화 생성 기술은 2일 이내의 구현 시간만이 소요된다.Contents I. Introduction..........................................................................1 II. Background..........................................................................4 2.1. NAND Flash Block Architecture..................................................4 2.2. NAND Cell Vth Distribution........................................................5 2.3. eFuse Operation of NAND Flash Chip.......................................6 III. Basic Idea and Background...............................................7 3.1. Basic Idea.......................................................................................7 3.2. Background: Variational Autoencoder........................................10 IV. Initial eFuse Generation: VAE-Based Dual Network....14 V. eFuse Optimization: Genetic Algorithm..........................17 VI. Experimental Results.........................................................21 6.1. Experimental Setup......................................................................21 6.2. Initial eFuse Generation Results................................................23 6.3. eFuse Optimization Results........................................................26 6.4. Discussion.....................................................................................29 VII. Related Work..................................................................31 VIII. Conclusion.......................................................................33Maste

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

Charge Trap Transistors (CTT): Turning Logic Transistors into Embedded Non-Volatile Memory for Advanced High-k/Metal Gate CMOS Technologies

While need for embedded non-volatile memory (eNVM) in modern computing systems continues to grow rapidly, the options have been limited due to integration and scaling challenges as well as operational voltage incompatibilities. Introduced in this work is a unique multi-time programmable memory (MTPM) solution for advanced high-k/metal-gate (HKMG) CMOS technologies which turns as-fabricated standard logic transistors into eNVM elements, without the need for any process adders or additional masks. These logic transistors, when employed as eNVM elements, are dubbed “Charge Trap Transistors” (CTTs). The fundamental device physics, principles of operation, and technological breakthroughs required for employing logic transistors as eNVM are presented. Implementation of CTT eNVM in 32 nm, 22 nm, 14 nm, and 7 nm production technologies has been realized and demonstrated in this work. The emerging memory technology landscape and the space that the CTT technology occupies therein are examined.The motivation behind this work is to develop an eNVM technology that is completely process/mask-free, multi-time programmable, operable at low/logic-compatible voltages, scalable, and secure. The CTT technology satisfies all of the aforementioned criteria. CTTs offer a data retention lifetime of > 10 years at 125 �C and an operation temperature range of -55�-125� C. Hardware results demonstrate an endurance of > 10^4 program/erase cycles which is more than adequate for most embedded applications. Hardware security enhancement, on-chip reconfigurable encryption, firmware, BIOS, chip ID, redundancy, repair at wafer and module test and in the field, performance tailoring, and chip configuration are a few of the applications of CTT eNVM. Moreover, the CTT array in its native (unprogrammed) state measures very well as an entropy source for potential PUF (Physically Unclonable Function) applications such as identification, authentication, anti-counterfeiting, secure boot, and cryptographic IP. In addition to the numerous digital applications, CTTs can also be utilized as an analog memory for applications like neuromorphic computing for machine learning (ML) and artificial intelligence (AI)

Embedded electronic systems driven by run-time reconfigurable hardware

Abstract This doctoral thesis addresses the design of embedded electronic systems based on run-time reconfigurable hardware technology –available through SRAM-based FPGA/SoC devices– aimed at contributing to enhance the life quality of the human beings. This work does research on the conception of the system architecture and the reconfiguration engine that provides to the FPGA the capability of dynamic partial reconfiguration in order to synthesize, by means of hardware/software co-design, a given application partitioned in processing tasks which are multiplexed in time and space, optimizing thus its physical implementation –silicon area, processing time, complexity, flexibility, functional density, cost and power consumption– in comparison with other alternatives based on static hardware (MCU, DSP, GPU, ASSP, ASIC, etc.). The design flow of such technology is evaluated through the prototyping of several engineering applications (control systems, mathematical coprocessors, complex image processors, etc.), showing a high enough level of maturity for its exploitation in the industry.Resumen Esta tesis doctoral abarca el diseño de sistemas electrónicos embebidos basados en tecnología hardware dinámicamente reconfigurable –disponible a través de dispositivos lógicos programables SRAM FPGA/SoC– que contribuyan a la mejora de la calidad de vida de la sociedad. Se investiga la arquitectura del sistema y del motor de reconfiguración que proporcione a la FPGA la capacidad de reconfiguración dinámica parcial de sus recursos programables, con objeto de sintetizar, mediante codiseño hardware/software, una determinada aplicación particionada en tareas multiplexadas en tiempo y en espacio, optimizando así su implementación física –área de silicio, tiempo de procesado, complejidad, flexibilidad, densidad funcional, coste y potencia disipada– comparada con otras alternativas basadas en hardware estático (MCU, DSP, GPU, ASSP, ASIC, etc.). Se evalúa el flujo de diseño de dicha tecnología a través del prototipado de varias aplicaciones de ingeniería (sistemas de control, coprocesadores aritméticos, procesadores de imagen, etc.), evidenciando un nivel de madurez viable ya para su explotación en la industria.Resum Aquesta tesi doctoral està orientada al disseny de sistemes electrònics empotrats basats en tecnologia hardware dinàmicament reconfigurable –disponible mitjançant dispositius lògics programables SRAM FPGA/SoC– que contribueixin a la millora de la qualitat de vida de la societat. S’investiga l’arquitectura del sistema i del motor de reconfiguració que proporcioni a la FPGA la capacitat de reconfiguració dinàmica parcial dels seus recursos programables, amb l’objectiu de sintetitzar, mitjançant codisseny hardware/software, una determinada aplicació particionada en tasques multiplexades en temps i en espai, optimizant així la seva implementació física –àrea de silici, temps de processat, complexitat, flexibilitat, densitat funcional, cost i potència dissipada– comparada amb altres alternatives basades en hardware estàtic (MCU, DSP, GPU, ASSP, ASIC, etc.). S’evalúa el fluxe de disseny d’aquesta tecnologia a través del prototipat de varies aplicacions d’enginyeria (sistemes de control, coprocessadors aritmètics, processadors d’imatge, etc.), demostrant un nivell de maduresa viable ja per a la seva explotació a la indústria

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

High-Density Solid-State Memory Devices and Technologies

This Special Issue aims to examine high-density solid-state memory devices and technologies from various standpoints in an attempt to foster their continuous success in the future. Considering that broadening of the range of applications will likely offer different types of solid-state memories their chance in the spotlight, the Special Issue is not focused on a specific storage solution but rather embraces all the most relevant solid-state memory devices and technologies currently on stage. Even the subjects dealt with in this Special Issue are widespread, ranging from process and design issues/innovations to the experimental and theoretical analysis of the operation and from the performance and reliability of memory devices and arrays to the exploitation of solid-state memories to pursue new computing paradigms