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研究生:林鴻貿
研究生(外文):Hung-Mao Lin
論文名稱:行奇偶與列選擇:內建自我測試之多掃描鏈環境下多重錯誤之診斷技術
論文名稱(外文):Column Parity and Row Selection (CPRS): A BIST Diagnosis Technique for Multiple Errors in Multiple Scan Chains
指導教授:李建模
指導教授(外文):Chien-Mo Li
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電子工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2005
畢業學年度:93
語文別:英文
論文頁數:54
中文關鍵詞:行奇偶列選擇多掃描鏈多重錯誤內建自我測試診斷
外文關鍵詞:Column ParityRow SelectionCPRSBISTDiagnosisMultiple ErrorsMultiple Scan Chains
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在本論文中,新的內建自我測試診斷技術被用於診斷多掃描鏈環境下之多重錯誤。在此技術中,每次掃描鏈輸出一組數據時,線性反饋移位暫存器會遮蔽部分數據。沒被遮蔽的數據,便被用於產生一組行奇偶與一組列奇偶。在每組掃描鏈數據輸出時,一個列奇偶便被產生。在簽名輸出時,行奇偶便被輸出。此技術也支援決定診斷。此技術在沒有未知數出現時,可以完全正確地診斷出錯誤所在點。而在有百分之一未知數出現情況下,此技術仍可以正確診斷出錯誤值地點。而此技術所需要的成本,只包含多餘的面積和額外的輸出接腳。
In this thesis, a BIST diagnosis technique is presented to diagnose multiple errors in multiple scan chains. An LFSR randomly selects outputs of multiple scan chains every scan cycle. The column parity and row parity of the selected scan outputs are observed every scan cycle and every scan unload, respectively. The deterministic diagnosis of multiple scan chain is also supported. In the absence of unknowns, CPRS correctly diagnoses all errors within 15 diagnosis sessions. Compared with other techniques, which diagnose no more than 15% errors, CPRS correctly diagnoses all errors in the presence of 1% unknowns. The cost of this technique is area overhead and one additional output observed every scan cycle.
摘要 I
誌謝 II
Abstract III
Acknowledge IV
TABLE OF CONTENT V
LIST OF FIGURE VII
LIST OF TABLE VIII
Chapter 1. Introduction 1
1.1 Motivation 1
1.2 CPRS 2
1.3 Contributions 3
1.4 Organization 4
Chapter 2. Background 5
2.1 Past research in BIST Diagnosis 5
2.1.1 Software category 5
2.1.2 Hardware category 5
2.2 Related work 8
2.2.1 Row and Column Parities 8
2.2.2 LFSR based random selection 10
Chapter 3. CPRS Diagnosis 12
3.1 Hardware Architecture 12
3.2 Diagnosis Flow 13
3.2.1 Random Diagnosis 13
3.2.2 Deterministic Diagnosis 15
3.3. Calculation Methods 15
3.3.1 Calculate Error Bits 15
3.3.2 RS-LFSR Seed Calculation 20
Chapter 4. Software Implementation 24
4.1 Generate SM, ECP, ERP, RSEM, and linear equations 24
4.1.1 Generate SM 24
4.1.2 Generate ECP, ERP 27
4.1.3 Generate RSEM 28
4.1.4 Generate Linear equations 30
4.2 Solve EM: Modulo-2 Gauss-Jordan elimination 32
4.3 Solve RS-LFSR seed using DSM 34
Chapter 5. Experiment Results 36
5.1 Total flow 36
5.2 Diagnosis without X 38
5.3 Diagnosis with X 41
Chapter 6. Discussions and Future work 44
6.1 Discussions 44
6.1.1 Hardware Overhead 44
6.1.2 Failing Pattern Identification 45
6.2 Future work 46
6.2.1 Probability analysis 47
6.2.2 DSM 47
6.2.3 BIST controller 48
Chapter 7. Summary 49
References 50
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