On-Demand Data Stream Controller for Integrated Circuit Test
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Solution Overview
Problem
Conventional techniques for embedding mission mode and test capabilities into integrated circuits significantly increase area, power, and complexity, making them unsuitable for cost-effective implementation in safety-critical applications, and result in higher Defective Parts-Per-Million (DPPM) due to increased logic size, which can lead to manufacturing defects and early life failures.
Innovation Solution
A scalable, modularized On-Demand Data Stream (ODDS) controller that stores programmable data streams on-chip, allowing repeatable and flexible issuance of data streams to targeted instruments, with data stream schedules and plans that can be executed in any order, reducing the need for external interfaces and minimizing chip area and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional techniques are used to embed mission mode and test capabilities into integrated circuits, then test functionality is achieved, but chip area, power consumption, and system complexity significantly increase
Solution Approach 1:
The ODDS controller is designed to perform multiple functions including mission mode operations, test pattern generation, and data streaming using a single unified architecture. This multi-functional approach eliminates the need for separate dedicated test hardware, thereby achieving comprehensive test capability while minimizing chip area and power consumption.
Solution Approach 2:
The system utilizes existing on-chip resources such as functional logic and data paths to perform self-testing and self-diagnosis during mission mode operations. By leveraging the circuit's own operational data and structures for testing purposes, external test equipment and additional test logic are minimized, reducing overall system complexity and area.
2Reliability
If conventional techniques are used to embed mission mode and test capabilities into integrated circuits, then test functionality is achieved, but power consumption and system complexity significantly increase
Solution Approach 1:
The ODDS controller performs both mission mode data streaming and test pattern generation using the same hardware resources. This eliminates the need for separate power-consuming test logic blocks, thereby achieving comprehensive test capability while minimizing power consumption through resource sharing.
Solution Approach 2:
The system performs self-testing using its own operational data streams and functional logic during normal operation. This approach avoids the need for additional powered test circuits and enables testing without increasing power consumption, as the same operational power supplies are utilized for both functional and test modes.
3Reliability
If conventional techniques are used to embed mission mode and test capabilities into integrated circuits, then test functionality is achieved, but system complexity significantly increases
Solution Approach 1:
The ODDS controller is designed as a universal engine that handles both mission mode operations and test pattern generation through a single integrated architecture. This eliminates the need for separate complex test control logic, reducing system complexity while maintaining comprehensive test capability.
Solution Approach 2:
The system leverages its own functional logic and data paths to perform self-testing, eliminating the need for external complex test equipment and additional test control circuits. This self-service approach significantly reduces system complexity by utilizing existing resources for testing purposes.
Data Source
AI summary
Embodiments relate generally to a scalable, modularized mechanism which allows for storing programmable data streams on chip and provides repeatable on-demand issuances of data streams to one or more targeted instruments. In some embodiments, multiple data streams are grouped into data stream schedules to perform a series of programmable operations on demand. In these and other embodiments, data stream schedules can be reused and further grouped into data stream plans that can be executed in any order upon request or are hard-coded in a specific order.