An Efficient Supervised Learning Method to Predict Power Supply Noise During At-speed Test

Mozaffari, Seyed Nima; Bhaskaran, Bonita; Narayanun, Kaushik; Abdollahian, Ayub; Pagalone, Vinod; Sarangi, Shantanu; Colburn, Jonathon E.

doi:10.1109/itc44170.2019.9000171

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…The most accurate results require electrical-level simulations with tools like SPICE [15]. However, it is too expensive to perform for more than a few clock cycles on large designs [14], [16].…”

Section: Dynamic Ir-drop Estimationmentioning

confidence: 99%

“…Nevertheless, [13] considers both location information and PDN structure to provide a highquality IR-drop profile and reveal severe regional IR-drop without incurring high computation overhead. Recently, there have been machine-learning-based methods targeting dynamic IR-drop estimation [14], [16], [19]. They can estimate IR-drop quickly and view IR-drop as a constraint to optimize test patterns or speedup engineer change order (ECO) iterations.…”

Section: Dynamic Ir-drop Estimationmentioning

confidence: 99%

“…The method in [14] depends on simulation results from commercial tools. [16] trains the model using IR-drop values measured on silicon. These machinelearning-based IR-drop estimations can be accurate based on their own application cases and are not likely to perform well on other new designs.…”

Section: Dynamic Ir-drop Estimationmentioning

confidence: 99%

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GPU-Accelerated Estimation and Targeted Reduction of Peak IR-Drop during Scan Chain Shifting

SHI,

HOLST,

WEN

2023

IEICE Trans. Inf. & Syst.

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High power dissipation during scan test often causes undue yield loss, especially for low-power circuits. One major reason is that the resulting IR-drop in shift mode may corrupt test data. A common approach to solving this problem is partial-shift, in which multiple scan chains are formed and only one group of scan chains is shifted at a time. However, existing partial-shift based methods suffer from two major problems:(1) their IR-drop estimation is not accurate enough or computationally too expensive to be done for each shift cycle; (2) partial-shift is hence applied to all shift cycles, resulting in long test time. This paper addresses these two problems with a novel IR-drop-aware scan shift method, featuring: (1) Cycle-based IR-Drop Estimation (CIDE) supported by a GPU-accelerated dynamic power simulator to quickly find potential shift cycles with excessive peak IR-drop; (2) a scan shift scheduling method that generates a scan chain grouping targeted for each considered shift cycle to reduce the impact on test time. Experiments on ITC'99 benchmark circuits show that: (1) the CIDE is computationally feasible; (2) the proposed scan shift schedule can achieve a global peak IR-drop reduction of up to 47%. Its scheduling efficiency is 58.4% higher than that of an existing typical method on average, which means our method has less test time.

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Section: Dynamic Ir-drop Estimationmentioning

confidence: 99%