SRAM is often chosen to be the process qualification vehicle during technology development or yield learning vehicle during product manufacturing, and consequently failure analysis of SRAM is the main feedback for process improvement and yield learning. The most common SRAM failure is single bit cell failure. Although its location can be precisely localized by functional test and the defect causing the failure is within the failing bit cell, its failure analysis becomes more and more challenging in advanced technology nodes. As semiconductor technology continuously scales down, SRAM bit cell size and power supply voltage decrease, resulting in increased transistor strength variation and mismatch. SRAM single bit cell soft failures have become more and more common. For such a failure, its defect is usually subtle or even there is not physical defect at most cases. The soft failure is just due to transistor parameter variation. To evaluate the single bit cell soft failure and identify its root cause, electrical nano-probing is an indispensable measure. In this paper, we will first describe the operation of a 6-Transistor (6-T) SRAM single bit cell and three different types of single bit cell soft failures, then discuss the two electrical nano-probing methods for the SRAM single bit cell soft failure.

Photon Emission Microscopy (PEM) is a popular technique for microelectronics failure analysis by detecting the photon emission from a defective circuit, when a failing device is electrically exercised at certain voltage. The photon emission contains physical location information, photon emission spectral information and photon emission intensity information. People often use the physical location information to localize a defective circuit and guide the follow-up physical failure analysis to find the defects. However, this procedure does not always work. Sometimes, it shows no defect found (NDF). In this paper, we propose a new computer vision-based analysis of the photon emission intensity for identifying the root cause of the excessively high IDDQ at elevated Vdds. The procedure includes collecting photon emissions at different Vdds and a follow-up photon emission intensity analysis with computer vision techniques. The procedure was applied on a case of microprocessor chip. After analyzing the dependencies of photon emission intensity on Vdd for 4 types of circuits, it was concluded that the SRAM circuit leakage is the root cause of the excessively high IDDQ at elevated Vdd.

Thermal Laser Stimulation (TLS) is employed extensively in semiconductor device fault isolation techniques such as TIVA (Thermal Induced Voltage Alteration), OBIRCH (Optical Beam Induced Resistance Change), SDL (Soft Defect localization), CPA (Critical Parameter Analysis), LADA (Laser Assisted Device Alteration), and LVI (Laser Voltage Imaging), etc. To investigate the TLS effects on 7nm FinFET transistor parameters, several transistors of 7nm FinFET inline ET (Electrical Test) macros were tested while employing TLS of various energy values. The test was done in linear mode so that the joule heating caused by the electrical current would be minimized. The experimental results showed that both NFETs and PFETs experienced increased Ioff (Off current) and Sub_Vt_lin_slope (Subthreshold slope), and decreased Ion (On current) and Vt_lin (Threshold voltage) due to elevated temperature of the transistor from TLS. Higher laser power caused greater effects on transistor parameters. The temperature increase on a transistor by TLS depends on the amount of laser energy transferred to, absorbed by, and dispersed by the transistor area. Factors such as the efficient coupling of the SIL (Solid Immersion Lens) with the Silicon backside surface, the transistor size, and the local layout around the transistor will greatly affect the amount of heat delivered to a particular transistor, even while using the same laser power. Thus, setting the laser power for fault isolation with TLS should consider these factors. Our experimental results also showed that the alteration of transistor parameters under TLS was not permanent if the laser power was carefully selected. It should be noticed that during dynamic fault isolation, a transistor may be switching between off, linear mode, and/or saturation mode. The temperature increase on the transistor under TLS may be higher than anticipated due to joule heating if the transistor operation is not confined to the linear region only. Experiments on transistors operating in saturation mode under TLS can be the subject of future work. The results obtained from these experiments can still establish guidelines for laser power settings to be used in the related fault isolation techniques for devices manufactured at the 7nm node so as to achieve non-destructive fault isolation.

Failure analysis plays a very important role in semiconductor industry. Photon Emission Microscopy (PEM) has been extensively used in localization of fails in microelectronic devices. However, PEM emission site is not necessarily at the location of the defect. Thus, it has limitation for the success rate of the follow-up physical failure analysis focusing on the emission site. As semiconductor technology advanced in the 3D FinFET realm and feature size further shrank down, the invisible defects during SEM inspection are tremendously increased. It leads to the success rate further decreasing. To maintain good success rate of failure analysis for advanced 3D FinFET technology, electrical probing is necessary to be incorporated into the failure analysis flow. In this paper, first, the statistic results of PEM emission sites versus real defect locations from 102 modules of microprocessors manufactured by 14nm 3D FinFET technology was present. Then, we will present how to wisely design electrical probing plan after PEM analysis. The electrical probing plans are tailored to different scan chain and ATPG failures of microprocessors for improving failure analysis success rate without increasing too much turn-around time. Finally, two case studies have been described to demonstrate how the electrical probing results guide the follow-up physical failure analysis to find the defect.

This paper describes an electrical and physical failure analysis methodology leading to a unique defect called residual EG oxide (shortened to REGO); which manifested in 14nm SOI high performance FinFET technology. Theoretically a REGO defect can be present anywhere and on any multiple Fin transistor, or any type of device (low Vt, Regular Vt or High Vt). Because of the quantum nature of the FinFET and REGO occurrence being primarily limited to single Fins, this defect does not impact large transistors with multiple FINs; moreover, REGO was found to only impact 3 Fin or less transistors. Since REGO can be present on any multi-FIN transistor the potential does exist for the defect to escape test screening. Subsequently a reliability BTI (Bias Temperature Instability) stress experiment by nanoprobing at contact level was designed to assess REGO’s potential reliability impact. The BTI stress results indicate that the REGO defect would not result in any additional reliability or performance degradation beyond model expectations.

This paper presents Electrical Failure Analysis (EFA) and Physical Failure Analysis (PFA) on a random time zero (t0) gate oxide defect within an IBM processor manufactured with a 14nm SOI (Silicon On Insulator) FinFET technology. The natures of the Functional Fail, the gate oxide defect, and the transistor characteristics are included. The impact of this gate oxide defect to product yield and performance, plus the extent to which it extends across the product chip, which includes passing circuits, is covered. Since chips, which may contain this defect, could be present within the entire product lifecycle, the reliability aspects of the defect at the transistor level were investigated. Among the various reliability stresses available for transistors, Constant Voltage Stress (CVS) Bias Temperature Instability (BTI) was chosen. CVS BTI stressing was able to be performed on both the NFETs and PFETs within the Inverter of the failing circuit, plus other identical reference circuits. The BTI stress nanoprobing is covered. This includes an overview of BTI stressing, confirming the nanoprobing system and electrical stress/test programs are adequate for BTI stressing, BTI stress methodologies for Inverters, plus the BTI stress results. The transistor level BTI stress results are discussed and compared to other published BTI literature. Finally, the reliability aspects of this gate oxide defect are discussed.

As semiconductor technology keeps scaling down, plus new structures of transistor and new materials introduction, not only are new failure mechanisms introduced, but also old classic failure mechanisms get evolved. The obvious example of failure mechanism evolution is short defect. In the previous technologies, although short defects can happen in different layers and appear in different forms, they always happens at intra-level. As semiconductor technology advanced into nanometer regime, short defect no longer only happened in intra-level, but also more and more often happened in interlevel. Failure analysis on the inter-level short defects is much more challenging because they are usually due to interaction of two processes, such as process variation in two process steps at the same location, and often hide in the bottom of tapered and dense patterns. The conventional PFA (Physical Failure Analysis) methodology often misses discovering the defect and then the defect will be removed by subsequent polishing. This paper has demonstrated some methods to tackle the challenges with three case studies of such inter-level short defects in nanometer semiconductor technologies.

Failure analysis plays an important role in yield improvement during semiconductor process development and device manufacturing. It includes two main steps. The first step is to find the defect and the second step is to identify the root cause. In the past, failure analysis mainly focused on the first step, namely how to find the defect for a failure; because in the previous generations of technology, once the defect was found, its root cause was relatively easy to be understood. As the current advanced semiconductor technology has become tremendously complicated, especially 3D devices, like FinFET, a defect found by failure analysis can be substantially transformed from its original defect by subsequent processes and can be totally different from its origin in size and shape. Thus, sometimes, the second step, identifying the root cause for a defect becomes more challenging and takes more time than the first step. With combination of failure analysis and inline inspection, it enables us to establish the relationship between the failure analysis defect and an in-line defect. This can link the defect for a device functional failure to its source layer and process step more quickly, leading to fast root cause identification. In this paper, the methodology was validated by fast identification of the root causes for three case studies in the latest FinFET technology.

The State-of-the-Art FinFET technology has been widely adopted in the industry, typically at 14 nm and below technology nodes. As fin dimensions are pushed into the nanometer scale, process complexity is highly escalated, posing great challenges for physical failure analysis. Meanwhile, the accelerated cycles of learning for new technology nodes demand high accuracy and fast turnaround time to solve the material and interface issues pertaining to semiconductor processing or device failure. In this paper, we report a case study of fin related defect that caused device failure. Several analytical techniques, namely, Scanning Electron Microscopy (SEM), plan-view and cross-section Transmission Electron Microscopy (TEM) with Energy Dispersive X-ray spectroscopy (EDX), Electron Energy Loss Spectroscopy (EELS) and Z-contrast tomography were employed to characterize the defect and identify root-cause, leading to the resolution of this issue.

Pin leakage continues to be on the list of top yield detractors for microelectronics devices. It is simply manifested as elevated current with one pin or several pins during pin continuity test. Although many techniques are capable to globally localize the fault of pin leakage, root cause analysis and identification for it are still very challenging with today’s advanced failure analysis tools and techniques. It is because pin leakage can be caused by any type of defect, at any layer in the device and at any process step. This paper presents a case study to demonstrate how to combine multiple techniques to accurately identify the root cause of a pin leakage issue for a device manufactured using advanced technology node. The root cause was identified as under-etch issue during P+ implantation hard mask opening for ESD protection diode, causing P+ implantation missing, which was responsible for the nearly ohmic type pin leakage.

As semiconductor technology keeps scaling down, the conventional physical failure analysis processes have faced increasing challenges and encountered low success rate. It is not only because the defect causing a failure becomes tinier and tinier, but also because some of these defects themselves are invisible. Electrical nano-probing with narrowing down a defect to a single transistor has greatly increased the likeliness of finding a tiny defect in subsequent TEM (transmission Electron Microscope) analysis. However, there is still an increasing trend of encountering an invisible defect at most advanced technology nodes. This paper will present how to identify the root causes of three such invisible defects with the combination of electrical nano-probing and TEM chemical analysis.

Process defects, either random or systematic, are often the top killers of any semiconductor device. Process defect learning and reduction are the main focuses in both technology development stage and product manufacturing yield ramp stage. In order to achieve fast defect learning, in-line defect inspection is implemented in critical layers during wafer manufacturing. In-line defect inspection is able to detect defects. However, in-line defect inspection alone cannot predict the impact of defects on device functional yield. Failure analysis is an effective method of finding the defects which really cause device functional failures. However, often, the defects found by failure analysis are very different from the original defects, making it difficult to understand the root cause. This paper will describe a methodology how to combine in-line defect inspection and failure analysis together to found the top killer defects and accelerate their root cause identification for fast defect learning and yield improvement.

Failure analysis for Static Random Access Memory (SRAM) is the major activity in any microelectronic failure analysis lab. Originating from SRAM array structure, SRAM failure can be simple as single bit, paired bit or quad bit failures, whose defect is located at the failure location, or complicated as logic type failure involving WL or BL patterns or entire blocks, whose defect is often not at the failure location. For such SRAM logic type failures, failure analysis is more challenging and detailed fault isolation is necessary prior to physical failure analysis. This paper has demonstrated how to use SRAM decoder scheme knowledge, detailed layout tracing and Photon Emission Microscope (PEM) analysis to deal with the challenges and find the root causes for several cases of SRAM logic type failures.

Fault localization on functional macros during advanced technology development requires a complex combination of tester based diagnostics and image based techniques including laser voltage imaging (LVI), laser voltage probing (LVP), critical parameter analysis (CPA) with laser stimulation and photon emission microscopy (PEM). These techniques are exemplified in the following three case studies. The first case involves a voltage sensitive SRAM block fail which was localized to a resistive via through the use of CPA, LVI and LVP. The second case demonstrates how a hard fail (a net-to-net metal short) in a scan chain was localized through use of tester based diagnostics, LVI, LVP and PEM. Finally, the last case shows how a condition sensitive failing latch chain was localized through CPA, LVI, LVP and PEM. Subsequent atomic force probing (AFP) identified source-drain leakage in one of the localized devices, and TEM analysis revealed a dislocation in the failing FET. Each of these cases demonstrates the value in utilizing tester based diagnostics along with laser based imaging and photon emission microscopy to localize failures.

This paper presents the successful use of the novel inline product-like logic vehicle (PATO) during the last technology development phases of IBM's 22nm SOI technology node. It provides information on the sequential PATO inline test flow, commonality analysis procedure, and commonality signature trending. The paper presents examples of systematic defects uniquely captured by the product-like back end of the line layout. Moreover, this complex logic vehicle also uncovered a rich Pareto of more than 20 types of systematic and random defect mechanisms across the front end of the line, the middle end of the line, and the back end of the line. And more importantly, the non-defect found rate was kept below 20%. This achievement was possible by: leveraging high volume inline test ATPG scan fail data through the novel commonality analysis approach; and selecting the highest ATPG confidence defects representing a known commonality signature to physical failure analysis.

With the microelectronic technology progresses in nanometer realm, like SRAM, logic circuits and structures are also becoming dense and more sensitive to process variation. Logic failures may have different root causes from SRAM failure. If these technology weak points for logic circuits are not detected and resolved during the technology development stage, they will greatly affect the product manufacturing yield ramp, leading to longer time of design to market. In this paper, we present a logic yield learning methodology based on an inline logic vehicle, which includes several scan chains of different latch types representative of product logic. Failure analysis for the low yield wafers had revealed several killer defects associated with logic circuits. A few examples of the systematic failures unique to logic circuits will be presented. In combination with SRAM yield learning, logic yield learning makes the technology development more robust thus improving manufacturability.

For SRAMs with high logic complexity, hard defects, design debug, and soft defects have to be tackled all at once early on in the technology development while innovative integration schemes in front-end of the line are being validated. This paper presents a case study of a high-complexity static random access memory (SRAM) used during a 32nm technology development phase. The case study addresses several novel and unrelated fail mechanisms on a product-like SRAM. Corrective actions were put in place for several process levels in the back-end of the line, the middle of the line, and the front-end of the line. These process changes were successfully verified by demonstrating a significant reduction of the Vmax and Vmin nest array block fallout, thus allowing the broader development team to continue improving random defectivity.