Semiconductor Memory Device Clock Phase Synchronization

The patent performs data training operations before normal data transfer to preliminarily establish proper timing relationships. The training mode pre-adjusts clock phases and data timing between the memory device and GPU, so that when normal high-speed operation begins, the timing is already optimized, preventing data transfer errors despite high speeds

The patent implements a feedback mechanism where the GPU detects data validity and sends feedback signals (such as write leveling information) back to the memory device. This feedback allows the system to continuously monitor and adjust timing relationships, ensuring that high-speed data transfer maintains reliability by compensating for any timing drift or phase mismatches that occur during operation

The patent introduces a data training mode as an intermediary operation between the memory device and GPU. This training mode acts as a mediator that establishes timing relationships and phase alignments between the separate clocks of the memory device and GPU, allowing them to work together harmoniously despite using different clock sources. The training sequence creates a common timing reference that bridges the two separate clock domains

The patent implements dynamic timing adjustment capabilities where clock phases and data timing can be adjusted based on actual operating conditions. The system can dynamically modify timing parameters during training operations and adapt to changing conditions during normal operation, allowing the separate clocks to remain synchronized through active adjustment rather than rigid fixed timing

The patent implements partial training operations where not all possible training sequences need to be executed to achieve sufficient timing alignment. The system can perform a subset of training patterns that are most critical for establishing timing relationships, rather than exhaustively testing all possible timing scenarios. This partial action approach achieves adequate reliability with reduced training time overhead

The data training patterns are executed as preliminary operations before normal data transfer begins. By performing these timing adjustments in advance, the system establishes optimal timing relationships upfront, allowing subsequent high-speed data transfer to proceed without additional timing adjustments. The time spent on training is invested beforehand to enable faster, more reliable operation during the actual data transfer phase