Applying Universal Chip Telemetry to Detect Latent Defects and Aging in Advanced Electronics

被引：4

作者：

Landman, A. Evelyn ^{[1
]}

Burlak, Alex ^{[2
]}

Sever, C. Nir ^{[3
]}

Hutner, D. Marc ^{[3
]}

机构：

[1] ProteanTecs, R&D, 36 Kdoshei Bagdad, IL-3303254 Haifa, Israel

[2] ProteanTecs, R&D, 1200 Route 22 East, Bridgewater, NJ 08807 USA

[3] ProteanTecs, Business, 36 Kdoshei Bagdad, IL-3303254 Haifa, Israel

来源：

2022 IEEE INTERNATIONAL RELIABILITY PHYSICS SYMPOSIUM (IRPS) | 2022年

关键词：

Reliability; aging; latent defects; functional safety; AI; advanced analytics; automotive; deep data; chip telemetry; continuous monitoring; degradation monitoring; DPPM; FinFET; predictive maintenance; outlier detection; physics of failure; machine learning; testing; OTA updates;

D O I：

10.1109/IRPS48227.2022.9764449

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Mission critical applications and zero downtime tolerant systems are dominating semiconductor consumption, with AI at the Datacenter, 5G and Automotive being implemented on leading edge FinFET silicon technologies and advanced 2.5D and 3D packaging, required to perform at increasing functionality and performance profiles, for extended lifetime durations. With these advanced technologies, comes a plethora of new concerns to the product health and reliability. Out of these applications, Automotive deserves special consideration being subject to additional regulatory and safety concerns. This paper will discuss how deep data analytics based on Universal Chip TelemetryT (UCT) offers a new approach to lifecycle health and performance monitoring in advanced electronics. By combining on-chip monitoring with machine learning inference in the cloud and edge, production quality is improved through advanced latent defect detection, while lifetime reliability is increased through degradation monitoring and predictive fault detection.

引用

页数：4