Improving Diagnosis Resolution with Population Level Statistical Diagnosis

In this paper, we present a diagnosis resolution improvement methodology for scan-based tests. We achieve 89% reduction in the number of suspect diagnosis locations and a 2.4X increase in the number of highly resolved diagnosis results. We suffer a loss in accuracy of 1.5%. These results were obtained from an extensive silicon study. We use data from pilot wafers and 11 other wafers at the leading-edge technology node and check against failure analysis results from 203 cases. This resolution improvement is achieved by considering the diagnosis problem at the level of a population (e.g. a wafer) of failing die instead of analyzing each failing die completely independently as has been done traditionally. Higher diagnosis resolution is critical for speeding up the yield learning from manufacturing test and failure analysis flows.

Energy-Efficient Production Planning Using a Two-Stage Fuzzy Approach

Analyzing energy consumption is an important task for a factory. In order to accomplish this task, most studies fit the relationship between energy consumption and product design features, process characteristics, or equipment types. However, the energy-saving effects of product yield learning are rarely considered. To bridge this gap, this study proposes a two-stage fuzzy approach to estimate the energy savings brought about by yield improvement. In the two-stage fuzzy approach, a fuzzy polynomial programming approach is first utilized to fit the yield-learning process of a product. Then, the relationship between monthly electricity consumption and increase in yield was fit to estimate the energy savings brought about by the improvement in yield. The actual case of a dynamic random-access memory factory was used to illustrate the applicability of the two-stage fuzzy approach. According to the experiment results, product yield learning can greatly reduce electricity consumption.

On Debugging Intermittent Chain Hold-time Failures Caused by Process Variations for FinFET Technology

Scan-based test has been the industrial standard method for screening manufacturing defects. Scan chains are vulnerable to most manufacturing defects and process variations. Therefore, chain failures diagnosis is critical for successful yield learning. However, traditional chain diagnosis requires failing masking patterns to identify faulty chains and their fault types for designs with test compression. In other words, it cannot diagnose the chain failures which don't fail the masking chain patterns. Unfortunately, advanced FinFET technologies with more manufacturing challenges and higher process variations may result in more subtle chain timing failures which can't be detected by chain masking patterns. In this work, we present a new debugging methodology, which combines chain diagnosis and tester-based test to effectively diagnose such intermittent chain failures. The proposed methodology is validated on silicon data for one modern large SOC design and successfully identified all scan cells with hold-time issues, which were validated by STA with corrected models. The subsequent mask fixes for these identified hold-time violations resolved this yield issue and dramatically improve the yield.

One-minute Preceptors in Action: Breast Radiologists and the Medical Student General Radiology Elective

Breast imaging, with its unique patient-facing, multimodality, and multidisciplinary workflow, offers opportunities to engage medical students enrolled in a general radiology rotation and to highlight the role of the radiologist in patient care. At a time when breast radiologists face unprecedented challenges in delivering safe and efficient imaging services, however, accommodating larger numbers of medical students can overwhelm reading rooms, dilute meaningful learning experiences for the student, and place further demands on faculty. In order to meet the students’ and clinician educators’ needs, Neher’s one-minute preceptor teaching strategy is used to create a high-yield learning environment in a short amount of time. In this model, the breast radiologist weaves together multiple impactful and varied learning experiences in only 8 to 12 total hours of structured student exposure during the 160-hour general radiology course. We describe our adaptation of this technique and the positive impact that a short breast imaging component had on our general radiology medical student rotation. This standardized curriculum is easily adapatable to a variety of learning styles. It contributes to medical students’ understanding of the various facets of radiology through direct participation and exceeds education goals set forth by the Alliance of Medical Student Educators in Radiology. Students’ evaluations of the general radiology rotation demonstrated a sharp uptick in the year following the adoption of the technique, and students’ rotation final examination mean scores on the breast questions were higher for students who participated at least eight hours on service in the breast radiology clinic.

Twelve tips for teaching neuroanatomy, from the medical students’ perspective (Preprint)

BACKGROUND Neuroanatomy is a complex and fascinating subject that is often a daunting prospect for medical students. In fact, the fear of learning neuroanatomy has gained its own name – “neurophobia”. OBJECTIVE To tackle “neurophobia” by summarising twelve tips for dynamic and engaging neuroanatomy teaching. METHODS Tips were formulated based on our experiences as three senior medical students and evidence-based techniques. RESULTS The 12 tips are (1) Big concepts before fine detail, (2) Draw an annotated diagram, (3) Teach form and function together, (4) Group anatomy into systems, (5) Teach the vasculature, (6) Familiarise students with neuroimaging, (7) Use dissections for haptic learning, (8) Teach from clinical cases, (9) Build from first principles, (10) Try working in reverse, (11) Let the student become the teacher, (12) Let the student become the examiner. CONCLUSIONS These 12 tips can be used by teachers and students alike to provide a high-yield learning experience.

Cerebrovascular Neurosurgery

Part of the Neurosurgery by Example series, this volume on cerebrovascular neurosurgery presents a wide variety of cases in which expert authors describe workup, diagnosis, surgical and endovascular procedures, and complication management of many vascular disorders of the brain and spine. It focuses on decision-making strategies and key “pivot points” in which management may change due to alternative presentations or situations, and it provides diagnostic and management “pearls” that emphasize crucial principles. Rather than an exhaustive review text, each chapter teaches nuanced management of cerebrovascular disease through a case example, including questions to aid in the reader’s understanding of each step. Containing a focused review of medical evidence, including selected key references for high-yield learning, Cerebrovascular Neurosurgery is appropriate for neurosurgeons who wish to learn more about this subspecialty and those preparing for the American Board of Neurological Surgery oral examination.