Improved Electrical Failure Analysis/Fault Isolation Tool Development on Server Motherboard Platforms Based on Historic Failure Modes

2004 ◽  
Author(s):  
Patrick G. Opdahl
Keyword(s):  
Failure Analysis ◽  
Failure Modes ◽  
Resource Constraints ◽  
Analysis Data ◽  
Fault Isolation ◽  
Tool Development ◽  
Success Rates ◽  
Complete Coverage ◽  
Electrical Failure ◽  
Silicon Devices

Abstract Electrical fault isolation constitutes the first steps in characterizing and isolating the failure modes and root causes of a failing motherboard. Ideally the Failure Analysis Test tools provide complete coverage of all motherboard buses and silicon devices. Time and resource constraints for tool development prevent complete coverage, however, so the challenge is to provide the highest level of debug test coverage in the shortest development schedule. A simplified Fault Isolation process has been created based on historical failure analysis data to reduce the development time and resources to create tools which allow diagnosing failure root causes on high-end server motherboards. This strategy prioritizes the most common types of electrical failure modes and the types of Electrical Failure Analysis / Fault Isolation (EFA-FI) tools best suited to diagnose these modes. The benefits of this strategy include shorter EFA-FI development times, equivalent success rates in failure root cause, lower costs, and more effective EFA-FI tools that can be used within the Design Team and at either OEM or Contract Manufacturing sites.

Latent Flash Single Bit and Multiple Bits Systematic Approach to Failure Analysis

2008 ◽  
Author(s):  
Hoang-Yen To ◽  
Dat Nguyen ◽  
Clyde Dunn ◽  
Detric Davis
Keyword(s):  
Failure Analysis ◽  
Failure Mechanism ◽  
Systematic Approach ◽  
Failure Modes ◽  
Fault Isolation ◽  
Wafer Fabrication ◽  
Electrical Failure ◽  
Non Volatile Memory ◽  
Different Temperatures ◽  
Volatile Memory

Abstract The flash considered for failure analysis in this paper is a non volatile memory with a NOR architecture in the array and a stacked gate for the bit cell. The flash failure was from data gain reported from various stages and at different temperatures after leaving the wafer fabrication. The failure can be single bit failure (SBF) or multiple bit failure (MBF). The FA process is comprised of two steps termed electrical failure analysis (EFA) and physical failure analysis (PFA). This paper discusses the method to differentiate failure modes and the efforts of fault isolation. Micro probing and nano probe characterization were important in the understanding of the failure mechanism. As seen in the EFA/PFA section, the reported SBF/MBF failures were actually due to a defect in the Mux and not at the bit cell.

SRAM Bitcell Defect Identification Methodology Using Electrical Failure Analysis Data

2020 ◽  
Author(s):  
Hyungtae Kim ◽  
Geonho Kim ◽  
Yunrong Li ◽  
Jinyong Jeong ◽  
Youngdae Kim
Keyword(s):  
Failure Analysis ◽  
Technology Development ◽  
New Technology ◽  
Random Access ◽  
Analysis Data ◽  
Test Method ◽  
Design For Test ◽  
Defect Identification ◽  
Electrical Failure ◽  
Efficient Test

Abstract Static Random Access Memory (SRAM) has long been used for a new technology development vehicle because it is sensitive to process defects due to its high density and minimum feature size. In addition, failure location can be accurately predicted because of the highly structured architecture. Thus, fast and accurate Failure Analysis (FA) of the SRAM failure is crucial for the success of new technology learning and development. It is often quite time consuming to identify defects through conventional physical failure analysis techniques. In this paper, we present an advanced defect identification methodology for SRAM bitcell failures with fast speed and high accuracy based on the bitcell transistor analog characteristics from special design for test (DFT) features, Direct Bitcell Access (DBA). This technique has the advantage to shorten FA throughput time due to a time efficient test method and an intuitive failure analysis method based on Electrical Failure Analysis (EFA) without destructive analysis. In addition, all the defects in a wafer can be analyzed and improved simultaneously utilizing the proposed defect identification methodology. Some successful case studies are also discussed to demonstrate the efficiency of the proposed defect identification methodology.

scholarly journals SRAM Bitcell Defect Identification Methodology Using Electrical Failure Analysis Data

2021 ◽  
Author(s):  
Hyungtae Kim ◽  
Geonho Kim ◽  
Yunrong Li ◽  
Jinyong Jeong ◽  
Youngdae Kim
Keyword(s):  
Failure Analysis ◽  
Technology Development ◽  
New Technology ◽  
Random Access ◽  
Analysis Data ◽  
Test Method ◽  
Design For Test ◽  
Defect Identification ◽  
Electrical Failure ◽  
Efficient Test

Abstract Static Random Access Memory (SRAM) has long been used for a new technology development vehicle because it is sensitive to process defects due to its high density and minimum feature size. In addition, failure location can be accurately predicted because of the highly structured architecture. Thus, fast and accurate Failure Analysis (FA) of the SRAM failure is crucial for the success of new technology learning and development. It is often quite time consuming to identify defects through conventional physical failure analysis techniques. In this paper, we present an advanced defect identification methodology for SRAM bitcell failures with fast speed and high accuracy based on the bitcell transistor analog characteristics from special design for test (DFT) features, Direct Bitcell Access (DBA). This technique has the advantage to shorten FA throughput time due to a time efficient test method and an intuitive failure analysis method based on Electrical Failure Analysis (EFA) without destructive analysis. In addition, all the defects in a wafer can be analyzed and improved simultaneously utilizing the proposed defect identification methodology. Some successful case studies are also discussed to demonstrate the efficiency of the proposed defect identification methodology.

scholarly journals SRAM Bitcell Defect Identification Methodology Using Electrical Failure Analysis Data

2021 ◽  
Author(s):  
Hyungtae Kim ◽  
Geonho Kim ◽  
Yunrong Li ◽  
Jinyong Jeong ◽  
Youngdae Kim
Keyword(s):  
Failure Analysis ◽  
Technology Development ◽  
New Technology ◽  
Random Access ◽  
Analysis Data ◽  
Test Method ◽  
Design For Test ◽  
Defect Identification ◽  
Electrical Failure ◽  
Efficient Test

Abstract Static Random Access Memory (SRAM) has long been used for a new technology development vehicle because it is sensitive to process defects due to its high density and minimum feature size. In addition, failure location can be accurately predicted because of the highly structured architecture. Thus, fast and accurate Failure Analysis (FA) of the SRAM failure is crucial for the success of new technology learning and development. It is often quite time consuming to identify defects through conventional physical failure analysis techniques. In this paper, we present an advanced defect identification methodology for SRAM bitcell failures with fast speed and high accuracy based on the bitcell transistor analog characteristics from special design for test (DFT) features, Direct Bitcell Access (DBA). This technique has the advantage to shorten FA throughput time due to a time efficient test method and an intuitive failure analysis method based on Electrical Failure Analysis (EFA) without destructive analysis. In addition, all the defects in a wafer can be analyzed and improved simultaneously utilizing the proposed defect identification methodology. Some successful case studies are also discussed to demonstrate the efficiency of the proposed defect identification methodology.

MBIST Driven SRAM Failure Analysis Using Laser Voltage Imaging and Laser Voltage Probing

2019 ◽  
Author(s):  
Jessica Yang ◽  
Omprakash Rengaraj ◽  
Puneet Gupta ◽  
Rudolf Schlangen
Keyword(s):  
Failure Analysis ◽  
Photon Emission ◽  
Random Access ◽  
Random Access Memory ◽  
Fault Isolation ◽  
Static Random Access Memory ◽  
Production Testing ◽  
Access Memory ◽  
Electrical Failure ◽  
Voltage Imaging

Abstract Static Random-Access Memory (SRAM) failure analysis (FA) is important during chip-level reliability evaluation and yield improvement. Single-bit, paired-bit, and quad-bit failures—whose defect should be at the failing bit-cell locations—can be directly sent for Physical Failure Analysis (PFA). For one or multiple row/column failures with too large of a suspected circuit area, more detailed fault isolation is required before PFA. Currently, Photon Emission Microscopy (PEM) is the most commonly used Electrical Failure Analysis (EFA) technique for this kind of fail [1]. Soft-Defect Localization / Dynamic Laser Stimulation (SDL/DLS) can also be applied on soft (Vmin) row/column fails for further isolation [2]. However, some failures do not have abnormal emission spots or DLS sensitivity and require different localization techniques. Laser Voltage Imaging (LVI) and Laser Voltage Probing (LVP) are widely established for logic EFA, [3] but require periodic activation via ATE which may not be possible using MBIST hardware and test-patterns optimized for fast production testing. This paper discusses the test setup challenges to enable LVI & LVP on SRAM fails and includes two case studies on <14 nm advanced process silicon.

Contact Leakage Failure Analysis by EBIC, C-AFM and Nano-probing

2019 ◽  
Author(s):  
Kuang-Tse Ho ◽  
Chien-Wei Wu ◽  
Te-Fu Chang ◽  
Chia-Hsiang Yen ◽  
Ching-Hsiang Chan
Keyword(s):  
Sample Preparation ◽  
Failure Analysis ◽  
Failure Mechanisms ◽  
Fault Isolation ◽  
Electrical Failure ◽  
Sem And Tem ◽  
Leakage Failure

Abstract This research sets up failure analysis flow to verify failure mechanisms and root causes of different kinds of contact leakage. This flow mainly uses EBIC, C-AFM and nano-probing to do fault isolation and confirm electrical failure mechanisms. Appropriate sample preparation is also mandatory for FIB, SEM and TEM inspection.

Targeted Silicon Ultra-Thinning by Contour Milling for Advanced Fault Isolation

2019 ◽  
Author(s):  
W. S. Teo ◽  
M.S. Wei ◽  
V. Narang ◽  
C. L. Gan ◽  
C. Richardson ◽  
...  
Keyword(s):  
Thermal Stability ◽  
Sample Preparation ◽  
Failure Analysis ◽  
Fault Isolation ◽  
Thin Sample ◽  
Electrical Failure ◽  
Contour Maps ◽  
Area Of Interest ◽  
Silicon Thickness ◽  
Thermal Stability Of

Abstract In this paper, we present methods for targeted silicon thinning by contour milling to overcome challenges associated with thinning large devices to under 5 µm remaining silicon thickness. Implementation of these techniques are expected to improve the yield of ultra-thin sample preparation and thermal stability of the device through electrical failure analysis for subsequent physical failure analysis. Using a computer numerical controlled milling system, the natural device bow is exploited to thin a specified area of interest by stage tilting before 2D milling. To target a larger area of interests, contour maps are rigged to thin an area preferentially while remaining compatible with existing workflows. Electrical testing have found improved thermal stability of the locally thinned samples over globally thinned samples.

Symptom or Cause? A Case Study

2015 ◽  
Author(s):  
Anuradha Swaminathan ◽  
Joy Liao ◽  
Howard Marks
Keyword(s):  
Failure Analysis ◽  
Continuous Wave ◽  
Analysis Data ◽  
Continuous Wave Laser ◽  
Electrical Failure ◽  
Root Cause ◽  
First Case ◽  
Successful Analysis ◽  
28 Nm

Abstract Although there are many advanced technologies and techniques for silicon diagnostics, effective failure analysis to root cause is getting increasingly challenging, as very often the electrical failure analysis data would point to a symptom that is the result of the defect rather than the actual location of the defect. Therefore, a combination of multiple techniques is often employed so that sensitivity of "the cause of the problem" can be observed. This work compiles a successful analysis with the aid of continuous wave laser voltage probing and soft defect localization techniques and presents three cases that are voltage-sensitive fails. The first case is a 28 nm device which failed at-speed scan. The second case is a 28 nm device failing RAM register BIST with high Vmin and the third case is a scan shift failure in a less than 28nm device.

scholarly journals Failure Case Studies of GaAs-Based Oxide-Confined VCSELs

2021 ◽  
Author(s):  
Kuang-Tse Ho ◽  
Cheng-Che Li
Keyword(s):  
Failure Analysis ◽  
Case Studies ◽  
Failure Modes ◽  
Fault Isolation ◽  
Analysis Procedure ◽  
Tem Analysis

Abstract This research summarizes a variety of physical failure modes of GaAs-based oxide-confined VCSELs and their root causes. Standard failure analysis procedure, which includes defect fault isolation by PEM or IR-OBIRCH and physical inspection by TEM analysis are also presented in detail.

Embedded SRAM Bitmapping and Failure Analysis for Manufacturing Yield Improvement

2000 ◽  
Author(s):  
Jean-Yves Glacet ◽  
Fourmun Lee
Keyword(s):  
Failure Analysis ◽  
Failure Modes ◽  
Monitoring Tool ◽  
Yield Improvement ◽  
Electrical Failure ◽  
Failure Data ◽  
Manufacturing Line ◽  
Significant Yield ◽  
Manufacturing Yield ◽  
Embedded Sram

Abstract SRAM bitmapping and failure analysis have been used as a driver for continuous yield improvement during pre-qualification manufacturing of a microcontroller. The combination of the embedded SRAM electrical failure data and failure analysis results was used to generate a pareto of failure modes and failure mechanisms and establish a correlation between the two. Bitmap trend charts can be used as a manufacturing line monitoring tool to supplement traditional in-line inspection. Identification of manufacturing issues can be obtained from bit failure information and compared with in-line inspection results to quickly identify which specific process module is responsible for a significant yield loss.

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