運用非監督式學習與感測數據之半導體機台異常檢測方法設計

Abstract

健康的設備對維持高生產率和高品質至關重要。線上異常檢測系統通過監視設備感測數據(ESD)來檢測維護需求和潛在影響產線良率的議題。半導體ESD的收集頻率和容量隨著感測器使用量逐漸增加而增加，這些感測器測量的量稱為狀態識別變量(SVID)。儘管近年來機器學習和自動化技術有長足進展，資深工程師仍面臨有效處理和利用大量湧入的ESD的新挑戰。他們對SVIDs、設備健康狀況、製程品質、產品良率之間的複雜關係缺乏全面的了解。因此，半導體廠迫切需要在自動化、快速、極小化ESD先驗知識需求的線上異常檢測上取得進展，從而促進主動的良率與作業管理。 本論文研究了非監督和快速ESD異常檢測問題(P)，並設計了解決方法(M)，以實現自動化和非監督的決策支持，具體如下： (P1) 如何從個別SVIDs的ESD中利用非監督學習，以在幾乎沒有先驗知識的情況下促進有效的線上異常檢測？ (M1) 頻域與時域自編碼器學習異常檢測(STALAD)框架和解決方案設計：主要挑戰為ESD的正常行為多樣且未知。我們設計了STALAD，包含四項創新：(1)識別因重複製程造成的ESD週期序列和頻域變換(CSST)，(2)利用堆疊自編碼器(SAEs)對ESD的CSST進行非監督學習以萃取數據主要特徵做為正常數據週期，(3)基於誤差序列的假設檢定以檢測異常，其中誤差序列是習得正常數據週期與待測感測數據週期間的差異，(4)動態程序控制，實現定期和並行的學習與檢定。 (P2) 如何在有限的實際數據下進行STALAD的效能評估？ (M2) 我們分析著名異常的特徵，並設計漂移、移位和尖點等模型，以生成任意大小的異常數據。此外，我們藉由調整現有實際數據的學習時段來操作數據集中正常/異常數據的比例，以評估STALAD的檢測時間及學習時對異常的容忍度。 (P3) 如何利用SVID間關聯性來達成更靈敏、更早的異常檢測？ (M3) 多感測器誤差序列融合異常檢測(MESFAD)檢定方案設計：工程師缺乏有效利用SVID間關聯性進行更有效異常檢測的知識。我們開發了MESFAD檢定方案，包含兩項創新：(1)均勻融合標準化誤差序列，以放大共現的異常訊號，相比於雜訊，(2)使用機率近似模型以在給定的誤警率下設置檢測門檻值。 (P4) 如何在非監督的情況下配對可能相關的SVIDs？ (M4) 基於頻域分析的對數-對數斜率相似性(SALSS)檢定方案設計：SVID間關聯性對ESD的影響的知識缺乏。我們開發了SALSS檢定方案，包含兩項創新：(1)使用對數-對數圖中的斜率和其信賴區間(CI)作為習得正常數據週期的振幅譜特徵，(2)分別對斜率進行t檢定和對CI寬度進行F檢定，以判斷兩SVIDs的振幅譜特徵相似性。具備相似振幅譜特徵的SVIDs將較有可能相關。 最終的貢獻和價值如下： (R1) STALAD 運用SAEs萃取量產工廠中各SVIDs數據在時域、頻域上正常數據週期的主要特徵，並設計異常檢測方法：STALAD無須數據標籤，便在一個案例研究數據集上達成100%準確率，且可在學習階段容忍最多33%異常數據，減少設備工程師篩選正常數據的工作量。緩慢漂移主要影響低頻振幅，雜訊則通常均勻影響所有振幅，故頻域STALAD可比時域STALAD更早檢測出緩慢漂移。作為控片晶圓監控製程參數的補充，STALAD可提前8天預警製程問題、避免數百片晶圓曝險、幫助製程工程師評估良率影響。 (R2) 設計異常模型與調整學習時段，使對STALAD的效能評估更加精確：著名的異常模型使我們得以評估STALAD對不易取得的小異常的靈敏度。調整現有數據中的學習時段可產生所期望的正常/異常的比例，以評估STALAD的檢測時間和在學習時對異常的容忍度。 (R3) MESFAD融合誤差序列以提升訊噪比並加速異常檢測：利用兩相關SVIDs中的異常共現性，MESFAD有更高的靈敏度和更快的檢測速度。案例研究表明，MESFAD可比設備工程師現行使用的個別SVIDs異常檢測方法提前395個週期(約22小時)檢測到異常。 (R4) SALSS利用對數-對數圖與假設檢定於振幅譜特徵，辨識出可能的SVID間關聯性：對相關SVIDs的研究顯示它們的振幅通常呈現冪衰減形式。SALSS正確辨識出案例研究數據集中的關聯性。結合使用SALSS與MESFAD，相比於未結合SALSS，可降低7.4%誤警率，同時維持100%靈敏度。

Healthy equipment is crucial for maintaining high productivity and quality. In-line anomaly detection (AD) systems monitor equipment sensory data (ESD) to detect maintenance needs and potential issues affecting production line yield. Semiconductor ESD is collected at high rates and volumes due to increased sensor usage. These sensor-measured quantities are known as Status Variable Identifications (SVID). Despite advancements in machine learning and automation over recent years, veteran engineers still face new challenges in effectively processing and exploiting the rapid influx and large volumes of ESD. They continue to lack comprehensive knowledge of the complex relationships among SVIDs, equipment health, process quality, and product yields. There are pressing needs for advancements in automated and prompt in-line AD with minimal prior ESD knowledge to facilitate proactive yield and operations management. This dissertation investigates unsupervised and prompt ESD AD problems (P) and designs methods (M) for an automated and unsupervised decision support as follows: (P1) How to exploit unsupervised learning from ESD of individual SVIDs to facilitate effective in-line AD with little prior knowledge? (M1) Spectral and Time Autoencoder Learning for Anomaly Detection (STALAD) framework and solution design: The key challenge is that normal behaviors of ESD are diversified but unknown. We design the STALAD framework which consists of four innovations: (1) identification of cycle series and spectral transformation (CSST) from ESD due to repetitive processing, (2) unsupervised learning from CSST of ESD by exploiting Stacked AutoEncoders (SAEs) for extracting major features of data as normal cycles, (3) hypothesis test for AD based on the error sequence, which is the difference between the learned normal cycle and the tested ESD cycle, (4) dynamic procedure control enabling periodic and parallel learning and testing. (P2) How to evaluate STALAD's performance under limited field data availability? (M2) We analyze the features of prominent anomalies and design models such as drift, shift, and spike to generate anomaly data of arbitrary sizes. Additionally, we manipulate the normal/abnormal ratio by adjusting the learning period in available field data to evaluate STALAD's detection time and its tolerance for anomaly data during learning. (P3) How to exploit SVID relations for more sensitive and earlier AD? (M3) Multi-sensor Error Sequence Fusion for Anomaly Detection (MESFAD) test scheme design: Engineers lack the knowledge to effectively exploit SVID relations for more effective AD. We develop the MESFAD test scheme, which consists of two innovations: (1) evenly fusing standardized error sequences to amplify co-occurring anomaly signals over noise, and (2) using a probabilistic approximation model to set the test threshold under a given false alarm rate. (P4) How to pair possibly related SVIDs without supervision? (M4) Spectral Analysis-based Log-log Slope Similarity (SALSS) test scheme design: Knowledge about the effects of SVID relations on ESD is lacking. We develop the SALSS test scheme, which consists of two innovations: (1) using the slope and the confidence interval (CI) of the slope in the log-log plot as magnitude features of the learned normal cycles, and (2) applying a t-test on the slope and an F-test on the width of the slope CI, respectively, to judge the similarity of magnitude features of two SVIDs. SVIDs with similar magnitude features will have higher possibility to be related. The resultant contributions and values are as follows: (R1) STALAD exploits SAEs to extract major features of normal cycles of individual-SVID ESD in mass production fabs in temporal and spectral domains, and design AD methods: STALAD achieves 100% accuracy on a case study dataset without data labels, and tolerates up to 33% anomaly data in the learning phase, reducing the effort needed for equipment engineers to filter normal data. Slow drifts primarily affect low-frequency magnitudes, while noise typically affects all magnitudes evenly. Hence, spectral STALAD can detect slow drifts earlier than temporal STALAD. Complementary to control wafer monitoring on process parameters, STALAD may warn of process issues 8 days earlier, preventing hundreds of wafers from risking defects and aiding process engineers in assessing yield impacts. (R2) Designing anomaly models and adjusting learning period enable more precise performance evaluation on STALAD: Prominent anomaly models enable the assessment of STALAD's sensitivity to small anomalies that are rarely available. Adjusting the learning period in available data can generate datasets with desired normal/abnormal ratio for evaluating STALAD's detection time and tolerance to anomalies. (R3) MESFAD's error sequence fusion for higher signal-to-noise ratio speeds up AD: MESFAD achieves higher sensitivity and faster detection speed by exploiting the co-occurrence of anomalies in two related SVIDs. A case study demonstrates MESFAD may detect anomalies 395 cycles (about 22 hours) earlier than detecting anomalies on individual SVIDs, which is the current practice for equipment engineers. (R4) SALSS's exploitation of log-log plots and hypothesis testing on magnitude features identifies possible SVID relations: Investigation on related SVIDs shows that their magnitudes often exhibit power decay patterns. SALSS correctly identifies relationships in a case study dataset. Combining SALSS with MESFAD reduces 7.4% false alarm while maintaining 100% sensitivity compared to not combining SALSS.