SAT-based Automatic Test Pattern Generation

Due to the rapidly growing size of integrated circuits, there is a need for new algorithms for Automatic Test Pattern Generation (ATPG). While classical algorithms reach their limit, there have been recent advances in algorithms to solve Boolean Satisfiability (SAT). Because Boolean SAT solvers are working on Conjunctive Normal Forms (CNF), the problem has to be transformed. During transformation, relevant information about the problem might get lost and therefore is not available in the solving process. In the following we briefly motivate the problem and provide the latest developments in the field. The technique was implemented and experimental results are presented. The approach was combined with the ATPG framework of NXP Semiconductors. Significant improvements in overall performance and robustness are demonstrated

Speeding up SAT-Based ATPG Using Dynamic Clause Activation

Abstract—SAT-based ATPG turned out to be a robust alter-native to classical structural ATPG algorithms such as FAN. The number of unclassified faults can be significantly reduced using a SAT-based ATPG approach. In contrast to structural ATPG, SAT solvers work on a Boolean formula in Conjunctive Normal Form (CNF). This results in some disadvantages for SAT solvers when applied to ATPG, e.g. CNF transformation time and loss of structural knowledge. As a result, SAT-based ATPG algorithms are very robust for hard-to-test faults, but suffer from the overhead for easy-to-test faults. We propose the SAT technique Dynamic Clause Activation (DCA) in order to reduce the run time gap between structural and SAT-based ATPG algorithms and, at the same time, retain the high level of robustness. Using DCA, the SAT solver works on a partial formula of a logic circuit which is dynamically extended during the search process using structural knowledge. Furthermore, efficient dynamic learning techniques can be easily integrated within the proposed technique. The approach is evaluated on large industrial circuits. Keywords-SAT; ATPG; Formal methods; CNF; I

SAT-based Automatic Test Pattern Generation

Abstract. Due to the rapidly growing size of integrated circuits, there is a need for new algorithms for Automatic Test Pattern Generation (ATPG). While classical algorithms reach their limit, there have been recent advances in algorithms to solve Boolean Satisfiability (SAT). Because Boolean SAT solvers are working on Conjunctive Normal Forms (CNF), the problem has to be transformed. During transformation, relevant information about the problem might get lost and therefore is not available in the solving process. In the following we briefly motivate the problem and provide the latest developments in the field. The technique was implemented and experimental results are presented. The approach was combined with the ATPG framework of NXP Semiconductors. Significant improvements in overall performance and robustness are demonstrated

Acute and late toxicity in prostate cancer patients treated by dose escalated intensity modulated radiation therapy and organ tracking

BACKGROUND: To report acute and late toxicity in prostate cancer patients treated by dose escalated intensity-modulated radiation therapy (IMRT) and organ tracking. METHODS: From 06/2004 to 12/2005 39 men were treated by 80 Gy IMRT along with organ tracking. Median age was 69 years, risk of recurrence was low 18%, intermediate 21% and high in 61% patients. Hormone therapy (HT) was received by 74% of patients. Toxicity was scored according to the CTC scale version 3.0. Median follow-up (FU) was 29 months. RESULTS: Acute and maximal late grade 2 gastrointestinal (GI) toxicity was 3% and 8%, late grade 2 GI toxicity dropped to 0% at the end of FU. No acute or late grade 3 GI toxicity was observed. Grade 2 and 3 pre-treatment genitourinary (GU) morbidity (PGUM) was 20% and 5%. Acute and maximal late grade 2 GU toxicity was 56% and 28% and late grade 2 GU toxicity decreased to 15% of patients at the end of FU. Acute and maximal late grade 3 GU toxicity was 8% and 3%, respectively. Decreased late > or = grade 2 GU toxicity free survival was associated with higher age (P = .025), absence of HT (P = .016) and higher PGUM (P < .001). DISCUSSION: GI toxicity rates after IMRT and organ tracking are excellent, GU toxicity rates are strongly related to PGUM

A Fast Untestability Proof for SAT-based ATPG

(ATPG) based on Boolean satisfiability (SAT) has been shown to be a beneficial complement to traditional ATPG techniques. Boolean solvers work on instances given in Conjunctive Normal Form (CNF). The required transformation of the ATPG problem into CNF is one main part of SAT-based ATPG and needs a significant portion of the overall run time. Solving the SAT instance is the other main part. Here, the time needed is often negligible – especially for untestable faults This paper presents a preprocessing technique that accelerates the classification of untestable faults. Those occur more frequently with increasing design sizes in industrial practice. In order to avoid overhead on testable faults, an untestability prediction is motivated. This increases the robustness of the entire ATPG process. The efficiency of the proposed method is shown during the experiments. I