Quiescent current testing of CMOS data converters

Power supply quiescent current (IDDQ) testing has been very effective in VLSI circuits designed in CMOS processes detecting physical defects such as open and shorts and bridging defects. However, in sub-micron VLSI circuits, IDDQ is masked by the increased subthreshold (leakage) current of MOSFETs affecting the efficiency of I¬DDQ testing. In this work, an attempt has been made to perform robust IDDQ testing in presence of increased leakage current by suitably modifying some of the test methods normally used in industry. Digital CMOS integrated circuits have been tested successfully using IDDQ and IDDQ methods for physical defects. However, testing of analog circuits is still a problem due to variation in design from one specific application to other. The increased leakage current further complicates not only the design but also testing. Mixed-signal integrated circuits such as the data converters are even more difficult to test because both analog and digital functions are built on the same substrate. We have re-examined both IDDQ and IDDQ methods of testing digital CMOS VLSI circuits and added features to minimize the influence of leakage current. We have designed built-in current sensors (BICS) for on-chip testing of analog and mixed-signal integrated circuits. We have also combined quiescent current testing with oscillation and transient current test techniques to map large number of manufacturing defects on a chip. In testing, we have used a simple method of injecting faults simulating manufacturing defects invented in our VLSI research group. We present design and testing of analog and mixed-signal integrated circuits with on-chip BICS such as an operational amplifier, 12-bit charge scaling architecture based digital-to-analog converter (DAC), 12-bit recycling architecture based analog-to-digital converter (ADC) and operational amplifier with floating gate inputs. The designed circuits are fabricated in 0.5 μm and 1.5 μm n-well CMOS processes and tested. Experimentally observed results of the fabricated devices are compared with simulations from SPICE using MOS level 3 and BSIM3.1 model parameters for 1.5 μm and 0.5 μm n-well CMOS technologies, respectively. We have also explored the possibility of using noise in VLSI circuits for testing defects and present the method we have developed

[Delta] IDDQ testing of a CMOS 12-bit charge scaling digital-to-analog converter

This work presents design, implementation and test of a built-in current sensor (BICS) for ∆IDDQ testing of a CMOS 12-bit charge scaling digital-to-analog converter (DAC). The sensor uses power discharge method for the fault detection. The sensor operates in two modes, the test mode and the normal mode. In the test mode, the BICS is connected to the circuit under test (CUT) which is DAC and detects abnormal currents caused by manufacturing defects. In the normal mode, BICS is isolated from the CUT. The BICS is integrated with the DAC and is implemented in a 0.5 μm n-well CMOS technology. The DAC uses charge scaling method for the design and a low voltage (0 to 2.5 V) folded cascode op-amp. The built-in current sensor (BICS) has a resolution of 0.5 μA. Faults have been introduced into DAC using fault injection transistors (FITs). The method of ∆IDDQ testing has been verified both from simulation and experimental measurements

Voltage sensing based built-in current sensor for IDDQ test

Quiescent current leakage test of the VDD supply (IDDQ Test) has been proven an effective way to screen out defective chips in manufacturing of Integrated Circuits (IC). As technology advances, the traditional IDDQ test is facing more and more challenges. In this research, a practical built-in current sensor (BICS) is proposed and the design is verified by three generations of test chips. The BICS detects the signal by sensing the voltage drop on supply lines of the circuit under test (CUT). Then the sensor performs analog-to-digital conversion of the input signal using a stochastic process with scan chain readout. Self-calibration and digital chopping are used to minimize offset and low frequency noise and drift. This non-invasive procedure avoids any performance degradation of the CUT. The measurement results of test chips are presented. The sensor achieves a high IDDQ resolution with small chip area overhead. This will enable IDDQ of future technology generations

Algorithms for Verification of Analog and Mixed-Signal Integrated Circuits

Over the past few decades, the tremendous growth in the complexity of analog and mixed-signal (AMS) systems has posed great challenges to AMS verification, resulting in a rapidly growing verification gap. Existing formal methods provide appealing completeness and reliability, yet they suffer from their limited efficiency and scalability. Data oriented machine learning based methods offer efficient and scalable solutions but do not guarantee completeness or full coverage. Additionally, the trend towards shorter time to market for AMS chips urges the development of efficient verification algorithms to accelerate with the joint design and testing phases. This dissertation envisions a hierarchical and hybrid AMS verification framework by consolidating assorted algorithms to embrace efficiency, scalability and completeness in a statistical sense. Leveraging diverse advantages from various verification techniques, this dissertation develops algorithms in different categories. In the context of formal methods, this dissertation proposes a generic and comprehensive model abstraction paradigm to model AMS content with a unifying analog representation. Moreover, an algorithm is proposed to parallelize reachability analysis by decomposing AMS systems into subsystems with lower complexity, and dividing the circuit's reachable state space exploration, which is formulated as a satisfiability problem, into subproblems with a reduced number of constraints. The proposed modeling method and the hierarchical parallelization enhance the efficiency and scalability of reachability analysis for AMS verification. On the subject of learning based method, the dissertation proposes to convert the verification problem into a binary classification problem solved using support vector machine (SVM) based learning algorithms. To reduce the need of simulations for training sample collection, an active learning strategy based on probabilistic version space reduction is proposed to perform adaptive sampling. An expansion of the active learning strategy for the purpose of conservative prediction is leveraged to minimize the occurrence of false negatives. Moreover, another learning based method is proposed to characterize AMS systems with a sparse Bayesian learning regression model. An implicit feature weighting mechanism based on the kernel method is embedded in the Bayesian learning model for concurrent quantification of influence of circuit parameters on the targeted specification, which can be efficiently solved in an iterative method similar to the expectation maximization (EM) algorithm. Besides, the achieved sparse parameter weighting offers favorable assistance to design analysis and test optimization

An efficient logic fault diagnosis framework based on effect-cause approach

Fault diagnosis plays an important role in improving the circuit design process and the manufacturing yield. With the increasing number of gates in modern circuits, determining the source of failure in a defective circuit is becoming more and more challenging. In this research, we present an efficient effect-cause diagnosis framework for combinational VLSI circuits. The framework consists of three stages to obtain an accurate and reasonably precise diagnosis. First, an improved critical path tracing algorithm is proposed to identify an initial suspect list by backtracing from faulty primary outputs toward primary inputs. Compared to the traditional critical path tracing approach, our algorithm is faster and exact. Second, a novel probabilistic ranking model is applied to rank the suspects so that the most suspicious one will be ranked at or near the top. Several fast filtering methods are used to prune unrelated suspects. Finally, to refine the diagnosis, fault simulation is performed on the top suspect nets using several common fault models. The difference between the observed faulty behavior and the simulated behavior is used to rank each suspect. Experimental results on ISCAS85 benchmark circuits show that this diagnosis approach is efficient both in terms of memory space and CPU time and the diagnosis results are accurate and reasonably precise

Machine Learning Methods For The Analysis Of Single-Cell And Spatially Resolved Transcriptomics Data

The advent of high-throughput next-generation sequencing technologies has transformed our understanding of cell biology and human disease. It is now common for investigators to study human cell populations by profiling the transcriptomes for thousands of single cells using single-cell RNA sequencing (scRNA-seq) technologies. In addition, recent advances in spatially resolved transcriptomics (SRT) technologies have enabled gene expression profiling with spatial information in tissues. Knowledge of the relative locations of different cells in a tissue is critical for understanding disease pathology because spatial information helps in understanding how the gene expression of a cell is influenced by its surrounding environment and how neighboring regions interact at the gene expression level. In order to take full advantage of the multi-modality information when analyzing scRNA-seq and SRT data, new methods are demanded for the following challenges: (1) how to identify cell types for scRNA-seq data with closely related cell types or low sequencing depths? (2) how to jointly model gene expression, spatial location, and histology in SRT data analysis? (3) how to increase gene expression resolution in SRT to study detailed tissue structure? In this dissertation, I seek to address these various challenges and difficulties associated with scRNA-seq and SRT data analyses. To address challenge (1), I developed ItClust, a supervised machine learning method that takes advantage of cell-type-specific gene expression information learned from a well-labeled source dataset, to help cluster and classify cell types on newly generated target data. To address challenge (2), I developed SpaGCN, a graph convolutional network approach that integrates gene expression, spatial location and histology to identify spatial domains and spatially variable genes in SRT data analysis. Lastly, to address challenge (3), I developed TESLA, a machine learning framework that enhances gene expression resolution in SRT and further performs multi-level tissue annotation with pixel-level resolution. I validated the utility of each of these approaches using experimentally validated cell type labels and independent pathologists’ annotation. I also demonstrated real use cases for these methods in deciphering tumor microenvironment in various cancer types

Development and application of methodologies and infrastructures for cancer genome analysis within Personalized Medicine

Programa de Doctorat en Biomedicina / Tesi realitzada al Barcelona Supercomputing Cener (BSC)[eng] Next-generation sequencing (NGS) has revolutionized biomedical sciences, especially in the area of cancer. It has nourished genomic research with extensive collections of sequenced genomes that are investigated to untangle the molecular bases of disease, as well as to identify potential targets for the design of new treatments. To exploit all this information, several initiatives have emerged worldwide, among which the Pan-Cancer project of the ICGC (International Cancer Genome Consortium) stands out. This project has jointly analyzed thousands of tumor genomes of different cancer types in order to elucidate the molecular bases of the origin and progression of cancer. To accomplish this task, new emerging technologies, including virtualization systems such as virtual machines or software containers, were used and had to be adapted to various computing centers. The portability of this system to the supercomputing infrastructure of the BSC (Barcelona Supercomputing Center) has been carried out during the first phase of the thesis. In parallel, other projects promote the application of genomics discoveries into the clinics. This is the case of MedPerCan, a national initiative to design a pilot project for the implementation of personalized medicine in oncology in Catalonia. In this context, we have centered our efforts on the methodological side, focusing on the detection and characterization of somatic variants in tumors. This step is a challenging action, due to the heterogeneity of the different methods, and an essential part, as it lays at the basis of all downstream analyses. On top of the methodological section of the thesis, we got into the biological interpretation of the results to study the evolution of chronic lymphocytic leukemia (CLL) in a close collaboration with the group of Dr. Elías Campo from the Hospital Clínic/IDIBAPS. In the first study, we have focused on the Richter transformation (RT), a transformation of CLL into a high-grade lymphoma that leads to a very poor prognosis and with unmet clinical needs. We found that RT has greater genomic, epigenomic and transcriptomic complexity than CLL. Its genome may reflect the imprint of therapies that the patients received prior to RT, indicating the presence of cells exposed to these mutagenic treatments which later expand giving rise to the clinical manifestation of the disease. Multiple NGS- based techniques, including whole-genome sequencing and single-cell DNA and RNA sequencing, among others, confirmed the pre-existence of cells with the RT characteristics years before their manifestation, up to the time of CLL diagnosis. The transcriptomic profile of RT is remarkably different from that of CLL. Of particular importance is the overexpression of the OXPHOS pathway, which could be used as a therapeutic vulnerability. Finally, in a second study, the analysis of a case of CLL in a young adult, based on whole genome and single-cell sequencing at different times of the disease, revealed that the founder clone of CLL did not present any somatic driver mutations and was characterized by germline variants in ATM, suggesting its role in the origin of the disease, and highlighting the possible contribution of germline variants or other non-genetic mechanisms in the initiation of CLL

Development and application of methodologies and infrastructures for cancer genome analysis within Personalized Medicine

[eng] Next-generation sequencing (NGS) has revolutionized biomedical sciences, especially in the area of cancer. It has nourished genomic research with extensive collections of sequenced genomes that are investigated to untangle the molecular bases of disease, as well as to identify potential targets for the design of new treatments. To exploit all this information, several initiatives have emerged worldwide, among which the Pan-Cancer project of the ICGC (International Cancer Genome Consortium) stands out. This project has jointly analyzed thousands of tumor genomes of different cancer types in order to elucidate the molecular bases of the origin and progression of cancer. To accomplish this task, new emerging technologies, including virtualization systems such as virtual machines or software containers, were used and had to be adapted to various computing centers. The portability of this system to the supercomputing infrastructure of the BSC (Barcelona Supercomputing Center) has been carried out during the first phase of the thesis. In parallel, other projects promote the application of genomics discoveries into the clinics. This is the case of MedPerCan, a national initiative to design a pilot project for the implementation of personalized medicine in oncology in Catalonia. In this context, we have centered our efforts on the methodological side, focusing on the detection and characterization of somatic variants in tumors. This step is a challenging action, due to the heterogeneity of the different methods, and an essential part, as it lays at the basis of all downstream analyses. On top of the methodological section of the thesis, we got into the biological interpretation of the results to study the evolution of chronic lymphocytic leukemia (CLL) in a close collaboration with the group of Dr. Elías Campo from the Hospital Clínic/IDIBAPS. In the first study, we have focused on the Richter transformation (RT), a transformation of CLL into a high-grade lymphoma that leads to a very poor prognosis and with unmet clinical needs. We found that RT has greater genomic, epigenomic and transcriptomic complexity than CLL. Its genome may reflect the imprint of therapies that the patients received prior to RT, indicating the presence of cells exposed to these mutagenic treatments which later expand giving rise to the clinical manifestation of the disease. Multiple NGS- based techniques, including whole-genome sequencing and single-cell DNA and RNA sequencing, among others, confirmed the pre-existence of cells with the RT characteristics years before their manifestation, up to the time of CLL diagnosis. The transcriptomic profile of RT is remarkably different from that of CLL. Of particular importance is the overexpression of the OXPHOS pathway, which could be used as a therapeutic vulnerability. Finally, in a second study, the analysis of a case of CLL in a young adult, based on whole genome and single-cell sequencing at different times of the disease, revealed that the founder clone of CLL did not present any somatic driver mutations and was characterized by germline variants in ATM, suggesting its role in the origin of the disease, and highlighting the possible contribution of germline variants or other non-genetic mechanisms in the initiation of CLL

Power supply partitioning for placement of built-in current sensors for IDDQ testing

IDDQ testing has been a very useful test screen for CMOS circuits. However, with each technology node the background leakage of chips is rapidly increasing. As a result it is becoming more difficult to distinguish between faulty and fault-free chips using IDDQ testing. Power supply partitioning has been proposed to increase test resolution by partitioning the power supply network, such that each partition has a relatively small defect-free IDDQ level. However, at present no practical partitioning strategy is available. The contribution of this thesis is to present a practical power supply partitioning strategy. We formulate various versions of the power supply partitioning problem that are likely to be of interest depending on the constraints of the chip design. Solutions to all the variants of the problem are presented. The basic idea behind all solutions is to abstract the power topology of the chip as a flow network. We then use flow techniques to find the min-cut of the transformed network to get solutions to our various problem formulations. Experimental results for benchmark circuits verify the feasibility of our solution methodology. The problem formulations will give complete flexibility to a test engineer to decide which factors cannot be compromised (e.g. area of BICS, test quality, etc) for a particular design and accordingly choose the appropriate problem formulation. The application of this work will be the first step in the placement of Built-In Current Sensors for IDDQ testing

Stem and progenitor cell involvement in acute lymphoblastic leukemia

Leukemic stem cells (LSCs) share the capacity of self renewal and extensive proliferation with normal hematopoietic stem cells (HSCs), and are therefore obvious targets for therapy. As such, they need to be identified and characterized in order to elucidate what drives them, and what separates them from their normal counterparts. The focus of this thesis is on pre B cell Acute Lymphoblastic Leukemia (ALL), the most common form of cancer in children. We have investigated 2 distinct subtypes of ALL, characterized by the gene fusions ETV6-RUNX1 (found mainly in pediatric ALL, conferring a favorable prognosis) and BCR-ABL1 (producing two different onco-proteins, designated P190 and P210, both of which are associated with a poor prognosis in both children and adults). We show that ETV6-RUNX1 ALL are propagated by B-cell committed LSCs expressing the lymphoid marker CD19, leaving the normal HSC compartment intact. In BCR-ABL1-positive ALL we show an unexpected difference between the two forms of the fusion protein, such that the LSC in P190 BCR-ABL1 ALL, similar to ETV6-RUNX1 ALL, are B-cell committed progenitors, whereas P210 BCR-ABL1 ALL originates in a multipotent HSC, expressing the same phenotypical markers as the normal HSC, and with a retained, albeit severely reduced, capacity to produce a clonal myeloid progeny. Interestingly, the LSC still displays the B cell commitment marker CD19, as only CD19+ cells propagates the disease in immunocompromised mice. We cannot, however, exclude very rare, and/or very quiscent CD19-ve P210 LSCs. This represents a hitherto unanticipated distinct biological difference between P190 and P210 ALLs, possibly indicating different requirements for eradication. In the second paper we describe a method to prospectively purify a large part of the leukemic cells from bone marrow or peripheral blood from patients with ALL, for relevant comparisons across samples. We compared ALL cells harvested from bone marrow and peripheral blood from the same patient by gene expression profiling, and found a striking similarity between cells from the two locations, indicating that bone marrow derived biological cues necessary for normal pre B cells not seem to segregate ALL cells in a blood and a bone marrow compartment, and that cells thus can be harvested from either compartment for further gene expression analyses. Finally, in the discussion part of the two papers, are preliminary data from follow up studies discussed, where we find indications for the existence of distinct sets of LSCs within the same patient with ALL or chronic myeloid leukemia in lymphoid blast crisis, contrary to the generally held view of a homogeneous LSC population