Invention:
This invention consists of a Meissner Effect Transistor (MET) consisting of a superconducting bridge engineered to retain a memory of past currents or voltages through magnetic flux pinning. Due to its ability to retain knowledge of current, the Meissner Effect Memory Resistor (MEMR) can serve the role of a memory synapse in artificial neural networks to solve machine learning problems, matching or exceed the capabilities of the human brain.
Background:
Memristors are electronic components whose resistance changes based on the amount and direction of charge that has flowed through them. They retain this resistance even when power is turned off, making them a form of non-volatile memory. Because they can both store data and perform computation, memristors are especially useful in neuromorphic systems that mimic how the brain processes information. Their ability to emulate synaptic behavior, perform analog signal processing, and enable ultra-dense memory arrays makes them a promising technology for next-generation computing, including AI acceleration, in-memory computing, and low-power electronics.
Applications:
- Construction of self-learning artificial networks
- Security and authentication
- Analog signal processing
- Cryogenic computing
- Neuromorphic systems
- Next-generation computing
Advantages:
- Non-volatile memory
- Magnetic control instead of electric and can operate in environments where electric interference is problematic
- Can operate at extremely high frequencies
- Low power usage
- Faster than digital neural networks
- Retain knowledge of past current
- Persistent memory and rapid self-learning
