使用快速預測器做低電壓降測試圖樣重新產生

Low-IR-drop Test Pattern Regeneration Using A Fast Predictor

劉世棠(Shi-Tang Liu)

指導教授 ： 李建模

國立臺灣大學/電機資訊學院/電子工程學研究所/碩士(2021年)

https://doi.org/10.6342/NTU202102739

摘要

在先進製程中，電壓降在測試領域成為一個重要的議題。因為電壓降造成的故障可能會導致測試的良率損失，所以我們提出一個低電壓降的測試圖樣重新產生技術去產生電壓降安全的測試圖樣。多數的傳統技術使用簡單的功耗度量去量化測試圖樣的安全性，而我們是直接使用電壓降作為標準。為了加速電壓降分析，我們使用了一個現有的機器學習模型去預測測試圖樣的電壓降。因為我們已經知道測試圖樣的電壓降，所以我們藉由這些測試圖樣去產生低電壓降偏好數值並抽取重要位元。藉由使用我們的技術，我們可以移除電壓降危險的圖樣並重新產生沒有預測電壓降違規的測試圖樣。實驗結果顯示我們的測試長度平均只有2.37%的成長，並且沒有錯誤覆蓋率的損失。最後我們為十個電壓降安全的圖樣做模擬，沒有電壓降違規被發現。

關鍵字

測試圖樣 ； 電壓降 ； 機器學習 ； 自動測試圖樣產生

並列摘要

IR-drop becomes an important issue for testing in advanced technology nodes. Because IR-drop-induced malfunction may lead to testing yield loss, we propose a low-IR-drop test pattern regeneration to produce IR-drop-safe patterns. Most traditional techniques use simple power metrics to quantify the safety of test patterns, but we use IR-drop as the criterion. To speed up IR-drop analysis, we apply an existing machine learning model to predict the IR-drop of test patterns. Because we already know the IR-drop of test patterns, we learn from test patterns to determine low-IR-drop preferred values and extract important bit assignments. By applying our techniques, we can remove IR-drop-risky patterns and regenerate test patterns without predicted IR-drop violations. Experimental results show that our test length overhead is only 2.37% on average, and there is no fault coverage loss. Finally, we perform accurate IR-drop simulation on 10 IR-drop-safe patterns and no IR-drop violations are found.

並列關鍵字

test pattern ； IR-drop ； machine learning ； ATPG

參考文獻

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