Cost-Effective TSV Grouping for Yield Improvement of 3D-ICs

Three-dimensional Integrated Circuits (3D-ICs) vertically stack multiple silicon dies to reduce overall wire length, power consumption, and allow integration of heterogeneous technologies. Through-silicon-vias (TSVs) which act as vertical links between layers pose challenges for 3D integration design. TSV defects can happen in fabrication process and bonding stage, which can reduce the yield and increase the cost. Recent work proposed the employment of redundant TSVs to improve the yield of 3D-ICs. This paper presents a redundant TSVs grouping technique, which partition regular and redundant TSVs into groups. For each group, a set of multiplexers are used to select good signal paths away from defective TSVs. We investigate the impact of grouping ratio (regular-to-redundant TSVs in one group) on trade-off between yield and hardware overhead. We also show probabilistic models for yield analysis under the influence of independent and clustering defect distributions. Simulation results show that for a given number of TSVs and TSV failure rate, careful selection of grouping ratios lead to achieving 100% yield at minimal hardware cost (number of multiplexers and redundant TSVs) in comparison to a design that does not exploit TSV grouping ratios

High-resolution remote thermography using luminescent low-dimensional tin-halide perovskites

While metal-halide perovskites have recently revolutionized research in optoelectronics through a unique combination of performance and synthetic simplicity, their low-dimensional counterparts can further expand the field with hitherto unknown and practically useful optical functionalities. In this context, we present the strong temperature dependence of the photoluminescence (PL) lifetime of low-dimensional, perovskite-like tin-halides, and apply this property to thermal imaging with a high precision of 0.05 {\deg}C. The PL lifetimes are governed by the heat-assisted de-trapping of self-trapped excitons, and their values can be varied over several orders of magnitude by adjusting the temperature (up to 20 ns {\deg}C-1). Typically, this sensitive range spans up to one hundred centigrade, and it is both compound-specific and shown to be compositionally and structurally tunable from -100 to 110 {\deg} C going from [C(NH2)3]2SnBr4 to Cs4SnBr6 and (C4N2H14I)4SnI6. Finally, through the innovative implementation of cost-effective hardware for fluorescence lifetime imaging (FLI), based on time-of-flight (ToF) technology, these novel thermoluminophores have been used to record thermographic videos with high spatial and thermal resolution.Comment: 25 pages, 4 figure

Event-triggered Learning

The efficient exchange of information is an essential aspect of intelligent collective behavior. Event-triggered control and estimation achieve some efficiency by replacing continuous data exchange between agents with intermittent, or event-triggered communication. Typically, model-based predictions are used at times of no data transmission, and updates are sent only when the prediction error grows too large. The effectiveness in reducing communication thus strongly depends on the quality of the prediction model. In this article, we propose event-triggered learning as a novel concept to reduce communication even further and to also adapt to changing dynamics. By monitoring the actual communication rate and comparing it to the one that is induced by the model, we detect a mismatch between model and reality and trigger model learning when needed. Specifically, for linear Gaussian dynamics, we derive different classes of learning triggers solely based on a statistical analysis of inter-communication times and formally prove their effectiveness with the aid of concentration inequalities

CO2 pipelines material and safety considerations

This paper presents an overview of some of the most important factors and areas of uncertainty affecting integrity and accurate hazard assessment of CO2 pipelines employed as part of the Carbon Capture and Sequestration (CCS) chain. These include corrosion, hydrate formation, hydrogen embrittlement and propensity to fast running ductile and brittle factures. Special consideration is given to the impact of impurities within the CO2 feed from the various capture technologies on these possible hazards. Knowledge gaps in the modelling of outflow and subsequent dispersion of CO2 following the accidental rupture of pressurised CO2 pipelines, central to their safety assessment, are also presented

Nanoscale magnetometry using a single spin system in diamond

We propose a protocol to estimate magnetic fields using a single nitrogen-vacancy (N-V) center in diamond, where the estimate precision scales inversely with time, ~1/T$, rather than the square-root of time. The method is based on converting the task of magnetometry into phase estimation, performing quantum phase estimation on a single N-V nuclear spin using either adaptive or nonadaptive feedback control, and the recently demonstrated capability to perform single-shot readout within the N-V [P. Neumann et. al., Science 329, 542 (2010)]. We present numerical simulations to show that our method provides an estimate whose precision scales close to ~1/T (T is the total estimation time), and moreover will give an unambiguous estimate of the static magnetic field experienced by the N-V. By combining this protocol with recent proposals for scanning magnetometry using an N-V, our protocol will provide a significant decrease in signal acquisition time while providing an unambiguous spatial map of the magnetic field.Comment: 8 pages and 5 figure

Conformability analysis for the control of quality costs in electronic systems

The variations embodied in the production of electronic systems can cause that system to fail to conform to its specification with respect to Critical to Quality features. As a consequence of such failures the system manufacture may incur significant quality costs ranging from simple warranty returns up to legal liabilities. It can be difficult to determine both the probability that a system will fail to meet its specification and estimate the associated cost of failure. This thesis presents the Electronic Conformability Analysis (eCA) technique a novel methodology and supporting tool set for the assessment and control of quality costs associated with electronic systems. The technique addresses the three main elements of production affecting quality costs associated with electronic systems which are functionality, manufacturability and testability. Electronic Conformability Analysis combines statistical performance exploration with process capability indices, a modified form of Failure Modes and Effects Analysis and a cost mapping procedure. The technique allows the quality costs associated with design and manufacture induced failures to be assessed and the effectiveness of test strategies in reducing these costs to be determined. Through this analysis of costs the technique allows the potential trade-offs between these costs and those associated with design and process modifications to be explored. In support of the Electronic Conformability Analysis technique a number of new analysis tools have been developed. These tools enable the methodology to cope with the specific difficulties associated with the analysis of electronic systems. The technique has been applied to a number of analogue and mixed signal, safety critical circuits from automotive systems. These case studies have included several different levels of system complexity ranging from relatively simple transistor circuits to highly complex mechatronic systems. These case studies have shown that the technique is effective in a commercial design and manufacturing environment

Estimation of a probability in inverse binomial sampling under normalized linear-linear and inverse-linear loss

Sequential estimation of the success probability $p$ in inverse binomial sampling is considered in this paper. For any estimator $\hat p$, its quality is measured by the risk associated with normalized loss functions of linear-linear or inverse-linear form. These functions are possibly asymmetric, with arbitrary slope parameters $a$ and $b$ for $\hat pp$ respectively. Interest in these functions is motivated by their significance and potential uses, which are briefly discussed. Estimators are given for which the risk has an asymptotic value as $p$ tends to $0$, and which guarantee that, for any $p$ in $(0,1)$, the risk is lower than its asymptotic value. This allows selecting the required number of successes, $r$, to meet a prescribed quality irrespective of the unknown $p$. In addition, the proposed estimators are shown to be approximately minimax when $a/b$ does not deviate too much from $1$, and asymptotically minimax as $r$ tends to infinity when $a=b$.Comment: 4 figure

제조 시스템에서의 예측 모델링을 위한 지능적 데이터 획득

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2021. 2. 조성준.Predictive modeling is a type of supervised learning to find the functional relationship between the input variables and the output variable. Predictive modeling is used in various aspects in manufacturing systems, such as automation of visual inspection, prediction of faulty products, and result estimation of expensive inspection. To build a high-performance predictive model, it is essential to secure high quality data. However, in manufacturing systems, it is practically impossible to acquire enough data of all kinds that are needed for the predictive modeling. There are three main difficulties in the data acquisition in manufacturing systems. First, labeled data always comes with a cost. In many problems, labeling must be done by experienced engineers, which is costly. Second, due to the inspection cost, not all inspections can be performed on all products. Because of time and monetary constraints in the manufacturing system, it is impossible to obtain all the desired inspection results. Third, changes in the manufacturing environment make data acquisition difficult. A change in the manufacturing environment causes a change in the distribution of generated data, making it impossible to obtain enough consistent data. Then, the model have to be trained with a small amount of data. In this dissertation, we overcome this difficulties in data acquisition through active learning, active feature-value acquisition, and domain adaptation. First, we propose an active learning framework to solve the high labeling cost of the wafer map pattern classification. This makes it possible to achieve higher performance with a lower labeling cost. Moreover, the cost efficiency is further improved by incorporating the cluster-level annotation into active learning. For the inspection cost for fault prediction problem, we propose a active inspection framework. By selecting products to undergo high-cost inspection with the novel uncertainty estimation method, high performance can be obtained with low inspection cost. To solve the recipe transition problem that frequently occurs in faulty wafer prediction in semiconductor manufacturing, a domain adaptation methods are used. Through sequential application of unsupervised domain adaptation and semi-supervised domain adaptation, performance degradation due to recipe transition is minimized. Through experiments on real-world data, it was demonstrated that the proposed methodologies can overcome the data acquisition problems in the manufacturing systems and improve the performance of the predictive models.예측 모델링은 지도 학습의 일종으로, 학습 데이터를 통해 입력 변수와 출력 변수 간의 함수적 관계를 찾는 과정이다. 이런 예측 모델링은 육안 검사 자동화, 불량 제품 사전 탐지, 고비용 검사 결과 추정 등 제조 시스템 전반에 걸쳐 활용된다. 높은 성능의 예측 모델을 달성하기 위해서는 양질의 데이터가 필수적이다. 하지만 제조 시스템에서 원하는 종류의 데이터를 원하는 만큼 획득하는 것은 현실적으로 거의 불가능하다. 데이터 획득의 어려움은 크게 세가지 원인에 의해 발생한다. 첫번째로, 라벨링이 된 데이터는 항상 비용을 수반한다는 점이다. 많은 문제에서, 라벨링은 숙련된 엔지니어에 의해 수행되어야 하고, 이는 큰 비용을 발생시킨다. 두번째로, 검사 비용 때문에 모든 검사가 모든 제품에 대해 수행될 수 없다. 제조 시스템에는 시간적, 금전적 제약이 존재하기 때문에, 원하는 모든 검사 결과값을 획득하는 것이 어렵다. 세번째로, 제조 환경의 변화가 데이터 획득을 어렵게 만든다. 제조 환경의 변화는 생성되는 데이터의 분포를 변형시켜, 일관성 있는 데이터를 충분히 획득하지 못하게 한다. 이로 인해 적은 양의 데이터만으로 모델을 재학습시켜야 하는 상황이 빈번하게 발생한다. 본 논문에서는 이런 데이터 획득의 어려움을 극복하기 위해 능동 학습, 능동 피쳐값 획득, 도메인 적응 방법을 활용한다. 먼저, 웨이퍼 맵 패턴 분류 문제의 높은 라벨링 비용을 해결하기 위해 능동학습 프레임워크를 제안한다. 이를 통해 적은 라벨링 비용으로 높은 성능의 분류 모델을 구축할 수 있다. 나아가, 군집 단위의 라벨링 방법을 능동학습에 접목하여 비용 효율성을 한차례 더 개선한다. 제품 불량 예측에 활용되는 검사 비용 문제를 해결하기 위해서는 능동 검사 방법을 제안한다. 제안하는 새로운 불확실성 추정 방법을 통해 고비용 검사 대상 제품을 선택함으로써 적은 검사 비용으로 높은 성능을 얻을 수 있다. 반도체 제조의 웨이퍼 불량 예측에서 빈번하게 발생하는 레시피 변경 문제를 해결하기 위해서는 도메인 적응 방법을 활용한다. 비교사 도메인 적응과 반교사 도메인 적응의 순차적인 적용을 통해 레시피 변경에 의한 성능 저하를 최소화한다. 본 논문에서는 실제 데이터에 대한 실험을 통해 제안된 방법론들이 제조시스템의 데이터 획득 문제를 극복하고 예측 모델의 성능을 높일 수 있음을 확인하였다.1. Introduction 1 2. Literature Review 9 2.1 Review of Related Methodologies 9 2.1.1 Active Learning 9 2.1.2 Active Feature-value Acquisition 11 2.1.3 Domain Adaptation 14 2.2 Review of Predictive Modelings in Manufacturing 15 2.2.1 Wafer Map Pattern Classification 15 2.2.2 Fault Detection and Classification 16 3. Active Learning for Wafer Map Pattern Classification 19 3.1 Problem Description 19 3.2 Proposed Method 21 3.2.1 System overview 21 3.2.2 Prediction model 25 3.2.3 Uncertainty estimation 25 3.2.4 Query wafer selection 29 3.2.5 Query wafer labeling 30 3.2.6 Model update 30 3.3 Experiments 31 3.3.1 Data description 31 3.3.2 Experimental design 31 3.3.3 Results and discussion 34 4. Active Cluster Annotation for Wafer Map Pattern Classification 42 4.1 Problem Description 42 4.2 Proposed Method 44 4.2.1 Clustering of unlabeled data 46 4.2.2 CNN training with labeled data 48 4.2.3 Cluster-level uncertainty estimation 49 4.2.4 Query cluster selection 50 4.2.5 Cluster-level annotation 50 4.3 Experiments 51 4.3.1 Data description 51 4.3.2 Experimental setting 51 4.3.3 Clustering results 53 4.3.4 Classification performance 54 4.3.5 Analysis for label noise 57 5. Active Inspection for Fault Prediction 60 5.1 Problem Description 60 5.2 Proposed Method 65 5.2.1 Active inspection framework 65 5.2.2 Acquisition based on Expected Prediction Change 68 5.3 Experiments 71 5.3.1 Data description 71 5.3.2 Fault prediction models 72 5.3.3 Experimental design 73 5.3.4 Results and discussion 74 6. Adaptive Fault Detection for Recipe Transition 76 6.1 Problem Description 76 6.2 Proposed Method 78 6.2.1 Overview 78 6.2.2 Unsupervised adaptation phase 81 6.2.3 Semi-supervised adaptation phase 83 6.3 Experiments 85 6.3.1 Data description 85 6.3.2 Experimental setting 85 6.3.3 Performance degradation caused by recipe transition 86 6.3.4 Effect of unsupervised adaptation 87 6.3.5 Effect of semi-supervised adaptation 88 7. Conclusion 91 7.1 Contributions 91 7.2 Future work 94Docto