In chip fabrication, spinner equipment is used for many steps such as creating a uniform layer of photo-resist or etching solvent over wafer. Spinner equipment works by rotating wafer at high speed (∼ 0.1 - 1 kHz) after depositing liquid solvent over wafer. The rotation causes solvent to distribute uniformly over wafer under ideal conditions. Due to impurities and rough terrain (chip layout), the spread is non-uniform. This results in spatially inconsistent chemical processing even though same pattern is printed throughout wafer. Process defects arise due to this non-uniformity. Currently, wafer inspection is done post the spinner process completion. Even though the spinner step is crucial, efficient real time quality tracking and in-line process control methods do not exist. In this paper, we present a novel hardware setup and corresponding software algorithm which makes the spinner track its process and dynamically adjust processing parameters (e.g. speed of rotation, tilt etc.) in order to improve the spread uniformity. We perform theoretical modelling of proposed novel hardware setup and present simulation result to prove its feasibility.

Material spectroscopy (MS) is used to identify elemental composition of micro particles. Energy dispersive X-Ray spectroscopy (EDX or EDS) is one such method. EDX analysis of defects found during wafer inspection aids in performing their root cause analysis (RCA). However, due to large processing time of EDX, it is applied very judiciously on a few chosen defects only. A wafer can typically contain ~100s of defects. The defect coverage of EDX is ~1% [1] thereby resulting in considerable gap in proper diagnosis and RCA. To overcome this issue, we demonstrate a soft method to perform MS of defects. The method predicts accurate elemental compositions of defect and background (~80%F1) when compared with EDX predictions on the same defect. The method is fast and could increase defect coverage for MS to ~100%• This can significantly improve RCA and thus help in Yield Enhancement (YE). Computing exact YE is complex as it involves many hidden and un-trackable factors. We perform theoretical high level modelling of more tangible factors i.e. profitability per month of Fab which is directly proportional to YE and theoretically show 14.6% improvement using our soft MS method.

INTRODUCTION: MURA defects in LED/LCD panels are one of the most challenging defects for Automatic Defect Classification and Localization (ADC) due to their extremely low contrast when compared with the background. Manual detection is subjective, error prone, very tedious and time consuming. Even when the type of MURA defects can be ascertained manually, the exact bounding box for defect is hard to determine. Various heuristic based image processingtechniques have been applied giving sub-optimal accuracy over generic datasets.OBJECTIVES: The primary objective of this paper is to check whether the state of the art DL (Deep Learning) network for general object classification and localization (MSCOCO PASCAL VOC etc.) can be applied successfully for MURA Defect Classification and Localization.METHODS: In this paper we present a single DL pipeline for classification and localization which for the first time is applied for MURA defects. Naive DL network - Single Shot multi-box Detector (SSD, pre-trained on ImageNet) was not sufficient to give a good F1 score because of the nature of the defect. Accuracy improved a little after applying various DLspecific optimization methods such as loss function optimization, network optimization etc. Utilizing the knowledge from MURA domain for data augmentation, like filtering based on image capture wavelength etc. improved the results significantly.RESULTS: Using optimization techniques that are from both DL domain as well as specific to MURA domain, we show improvement in the accuracy of the base DL pipeline from ~30% to ~80%. Minimum heuristics were used to define the pipeline so that it can easily adapt to any new MURA dataset. The paper shows the importance of domain specific preprocessing steps for the designed network in case of MURA defects.CONCLUSION: Using DL, MURA classification and localization had not been tried before. For the first time we demonstrated results for both classification and localization of MURA defects using state-of-the-art DL network with F1~80%. We also conclude that state-of-the-art network for general object detection can be reused with the help of Transfer Learning (TL) concept and fine-tuned with MURA domain specific optimizations mentioned in paper for optimalperformances in MURA domain.

A typical scene under the camera lens of a wafer inspection equipment consists of device features that are at different height relative to each other. To image such a scene, conventional camera systems within the equipment apply a single focus setting to all the features in the field of view. The resulting images consist of blurred regions corresponding to features that are not located at the object distance conforming to the lens focus setting. Such defocused images reduce the accuracy of defect detection process and adversely affect wafer yield. In this paper, we present a method to overcome this limitation using spatial light modulator (SLM). By optimizing the voltage to the cells of SLM, it can be converted into an array of partitioned thin lenses. Using this unique property of the SLM, we describe a process for obtaining "all-in-focus", high resolution image of the entire field of view. Our simulation results show clearly the benefit of our solution.