Semiconductor Operation Models for Hazardous Area Classification
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Solution Overview
Problem
Existing methods for classifying hazardous environments rely on expensive custom-built sensors, which are not scalable and inefficient for widespread use in semiconductor devices.
Innovation Solution
A global operation model, such as an artificial neural network (ANN), is trained using federated learning across decentralized semiconductor devices to infer the safety critical nature of an environment based on semiconductor device characteristics, eliminating the need for dedicated sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive custom-built sensors are used to detect environmental safety critical conditions, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The semiconductor device uses its own operational characteristics (current, voltage, temperature) during normal operation to infer environmental conditions. The device serves itself by leveraging its intrinsic properties rather than requiring external sensing components, thereby eliminating the need for expensive custom sensors while maintaining detection capability.
Solution Approach 2:
The semiconductor device performs multiple functions: it executes applications, and simultaneously serves as an environmental sensor through its operational characteristics. By making the device universal, it can detect hazardous conditions without dedicated sensing hardware, resolving the contradiction between measurement precision and device complexity.
2Reliability
If dedicated sensors are deployed to monitor environmental conditions, then reliability of safety detection is improved, but loss of energy increases
Solution Approach 1:
The system extracts safety monitoring capability from separate energy-consuming sensors and integrates it into the semiconductor device's own operational parameters. By using the device's existing power consumption and thermal characteristics during normal operation, the system achieves reliable safety detection without the additional energy burden of dedicated sensing hardware.
Solution Approach 2:
The patent merges the safety monitoring function with the existing semiconductor device operation. Instead of adding separate sensor systems that would consume additional energy, the system combines environmental detection with the device's normal computational and operational activities, thereby maintaining reliability while minimizing energy loss.
3Adaptability or versatility
If multiple semiconductor devices are used for distributed training, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system uses copies of the same semiconductor device architecture deployed across multiple locations. Each device runs identical or similar applications and generates comparable operational data, enabling distributed training that improves model adaptability to various environmental conditions while avoiding the complexity of heterogeneous device systems.
Solution Approach 2:
The training process is segmented and distributed across multiple semiconductor devices, with each device contributing local operational data to a centralized model. This segmentation allows the system to achieve high adaptability through distributed intelligence while keeping individual device complexity manageable, as each device performs similar standardized functions.
Data Source
AI summary
The present disclosure includes apparatuses, methods, and systems for classifying an area as hazardous or non-hazardous based on an operation of a semiconductor device. In an example, an apparatus can include a memory configured to store a global operation model and a processor coupled to the memory wherein the processor is configured to receive test data and operating data from a semiconductor device based on operation of the semiconductor device in an area, run the global operation model on the test data and the operating data from the semiconductor device to generate output data, and classify the area as hazardous or non-hazardous based on the output data.