Characterization and estimation of circuit reliability degradation under NBTI using on-line IDDQ measurement

• Published in: 2007 44th ACM/IEEE Design Automation Conference pp. 358 - 363
• Main Authors: Kang, Kunhyuk, Kim, Keejong, Islam, Ahmad E., Alam, Muhammad A., Roy, Kaushik
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 04.06.2007; IEEE
• Series: ACM Conferences
• Subjects: Circuit testing; Degradation; Frequency; Hardware > Hardware test; Hardware > Hardware validation; Hardware > Robustness; IDDQ; Life estimation; Lifetime estimation; MOSFETs; NBTI; Negative bias temperature instability; Niobium compounds; Random access memory; Reliability; Reliability Characterization; Titanium compounds; reliability characterization; NBTI
• ISBN: 1595936270; 9781595936271
• ISSN: 0738-100X
• DOI: 10.1145/1278480.1278572

Negative bias temperature instability (NBTI) in MOSFETs is one of the major reliability challenges in nano-scale technology. This paper presents an efficient technique to characterize and estimate the lifetime circuit reliability under NBTI degradation. Unlike conventional approaches, where a representative fMAX (maximum operating frequency) measurement from timing critical circuitry is used, we propose to utilize the standby circuit leakage IDDQ as a metric to detect and characterize temporal NBTI degradation in digital circuits. Compared to the fMAX based approach, the proposed IDDQ based technique benefits from lower test cost and improved capability of estimating reliability of complex circuitries such as ALUs and SRAM arrays. We have derived an analytical expression for circuit IDDQ from the analytical PMOS Vt degradation model (ΔVt ∝ t1/6). The proposed model is verified with measurement data obtained from a test chip fabricated in 130nm technology. Furthermore, we examine the possible applications of our proposed IDDQ based NBTI characterization. We show that the temporal degradation in static noise margin (SNM) of SRAM array and fMAX of random logic circuits are highly correlated to the IDDQ measurement, and this relationship can be used to predict long term circuit reliability.