GBT is Implementing Machine Learning Driven, Pattern Matching Technology for its Epsilon, Mi-crochip Reliability Verification and Correction EDA Tool

GBT Technologies Inc. is implementing a machine learning driven, pattern matching technology within its Epsilon, microchip’s reliability verification and correction Electronic Design Automation (EDA) tool. Design rules are getting increasingly complex with each new process node and design firms are facing new challenges in the physical verification domain. One of the major areas that are affected by the process physics, is reliability Verification (RV). Microchips are major components nearly in every major electronics application. Civil, military and space exploration industries require reliable operations for many years, and in severe environments. High performance computing systems require advanced processing with high reliability to ensure the consistency and accuracy of the processed data. Complex integrated circuits are in the heart of these systems and need to function with high level of dependability. Particularly in the fields of medicine, aviation, transportation, data storage and industrial instrumentation, microchip’s reliability factor is crucial. GBT is implementing new machine learning driven, pattern matching techniques within its Epsilon system with the goal of addressing the advanced semiconductor’s physics, ensuring high level of reliability, optimal power consumption and high performance. As Epsilon analyzes the layout of an integrated circuit (IC), it identifies reliability weak spots, which are specific regions of an IC’s layout, and learns their patterns. As the tool continues analyzing the layout it records problematic zones taking into account the pattern’s orientations and placements. In addition, it is designed to understand small variations in dimensions of the pattern, as specified by the designer or an automatic synthesis tool. As the weak spots are identified, the tool will take appropriate action to modify and correct them. A deep learning mechanism will be performing the data analysis, identification, categorization, and reasoning while executing an automatic correction. The Machine Learning will understand the patterns and record them in an internal library for future use. Epsilon’s pattern matching technology will be analyzing the chip’s data according to a set of predefined and learned-from-experience rules. Its cognitive capabilities will make it self-adjust to newest nodes with new constraints and challenges, with the goal of providing quick and reliable verification and correction of an IC layout.

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“The ability to analyze and address advanced IC’s reliability parameters is necessary to mitigate risk of system degradation, overheating, and possible malfunction. It can affect microchip’s performance, power consumption, data storage and retrieval, heat and an early failure which may be critical in vital electronic systems. Epsilon analyzes a microchip data for reliability, power and electrothermal characteristics, and performs auto-correction in case violations found. We are now implementing an intelligent technology for Epsilon with the goal of utilizing pattern matching algorithms to formulate a smart detection of reliability issues within integrated circuits layout. The new techniques will analyze and learn weak spots within microchip’s data, predicting failure models that are based on the process’ physics and electrical constraints knowledge. It will take into consideration each device’s function, connectivity attributes, electrical currents information, electrothermal factors and more to determine problematic spots and perform auto-correction. Particularly for FinFet and GAA FET (Gate All Around FET) technologies, a device’s functionality is developed with major reliability considerations ensuring power management efficiency, optimal thermal analysis aiming for long, reliable life span. Using smart pattern matching methods, we plan to improve reliability analysis, achieving consistency and accuracy across designs within advanced manufacturing processes. As dimensions of processes shrink, IC’s layout features become much more complex to analyze for electrical phenomenon. To provide an intelligent answer for these complexities, we are implementing deep learning-based pattern matching technology with the goal of ensuring efficient, ‘green’ microchip’s power consumption, higher performance, optimized thermal distribution, and ultimately superior reliability” stated Danny Rittman, the Company’s CTO.

There is no guarantee that the Company will be successful in researching, developing or implementing this system. In order to successfully implement this concept, the Company will need to raise adequate capital to support its research and, if successfully researched and fully developed, the Company would need to enter into a strategic relationship with a third party that has experience in manufacturing, selling and distributing this product. There is no guarantee that the Company will be successful in any or all of these critical steps.

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GBT Technologies, Inc. (OTC PINK: GTCH) (“GBT”) (http://gbtti.com) is a development stage company which considers itself a native of Internet of Things (IoT), Artificial Intelligence (AI) and Enabled Mobile Technology Platforms used to increase IC performance. GBT has assembled a team with extensive technology expertise and is building an intellectual property portfolio consisting of many patents. GBT’s mission, to license the technology and IP to synergetic partners in the areas of hardware and software. Once commercialized, it is GBT’s goal to have a suite of products including smart microchips, AI, encryption, Blockchain, IC design, mobile security applications, database management protocols, with tracking and supporting cloud software (without the need for GPS). GBT envisions this system as a creation of a global mesh network using advanced nodes and super performing new generation IC technology. The core of the system will be its advanced microchip technology; technology that can be installed in any mobile or fixed device worldwide. GBT’s vision is to produce this system as a low cost, secure, private-mesh-network between all enabled devices. Thus, providing shared processing, advanced mobile database management and sharing while using these enhanced mobile features as an alternative to traditional carrier services.

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[To share your insights with us, please write to sghosh@martechseries.com]

The post GBT is Implementing Machine Learning Driven, Pattern Matching Technology for its Epsilon, Mi-crochip Reliability Verification and Correction EDA Tool appeared first on AiThority.

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