Tensor Nonlinear Signal-Processing RAM for Multi-State Setpoints
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Solution Overview
Problem
Existing plasma processing systems struggle with nonlinear impedance changes, requiring complex and inefficient methods to manage multi-dimensional signal processing tasks, especially in applications like dielectric etch that demand four or more states in setpoint signals, which prior art systems cannot accommodate without hardware upgrades.
Innovation Solution
The introduction of tensor non-linear signal processing RAM (TSP-RAM) systems that can process multi-dimensional arrays and tensors intrinsically, enabling multi-dimensional digital signal processing without dimensionality reduction, supporting up to 16 states with software upgrades and scalable to infinite states on advanced hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional RAM systems are used for plasma processing control, then hardware complexity is reduced, but the system cannot accommodate four or more states in setpoint signals required by emerging plasma processing applications
Solution Approach 1:
The patent transitions from traditional scalar or vector signal processing to tensor-based multi-dimensional signal processing. This enables the system to handle four or more states by utilizing multi-dimensional tensor structures, allowing plasma processing systems to accommodate complex multi-state setpoint signals without increasing hardware complexity.
Solution Approach 2:
The patent changes the fundamental parameter representation from scalar values to tensor structures with multiple dimensions. This parameter transformation allows the system to encode multiple states (four or more) within a single tensor object, enabling emerging plasma processing applications to achieve higher state complexity without additional hardware components.
2Productivity
If dimensionality reduction methods are used for signal processing, then computational efficiency is improved, but processing accuracy for multi-dimensional tensors is degraded
Solution Approach 1:
The patent implements a universal tensor processing architecture that can handle multi-dimensional tensors of various ranks and dimensions without requiring dimensionality reduction. This multi-functional approach maintains processing accuracy for complex tensors while achieving computational efficiency through optimized tensor operations that work directly with high-dimensional data structures.
Solution Approach 2:
The patent uses tensor copy and transfer mechanisms to move multi-dimensional tensor data between memory and processing units without loss of dimensional information. This allows accurate preservation of multi-dimensional signal characteristics while enabling efficient processing through standardized tensor operation routines that maintain precision across all dimensions.
3Adaptability or versatility
If hardware upgrades are implemented to support more states, then adaptability is improved, but device complexity and cost increase
Solution Approach 1:
The patent implements dynamic state configuration through software-controlled tensor parameters rather than fixed hardware configurations. This allows the system to adaptively support four or more states by changing software parameters and tensor definitions, eliminating the need for physical hardware upgrades and reducing both device complexity and cost while maintaining full adaptability.
Data Source
AI summary
Various illustrative aspects are directed to a system that comprises a tensor data pre-processing circuit and a tensor data write/read circuit. One or more output ports of the tensor data pre-processing circuit are operably coupled to one or more input ports of the tensor data write/read circuit.