Method for ex-situ training in memristor-based neuromorphic circuit using robust weight programming method

A feedback-based weight programming method for a high-density crossbar without the use of any transistor or diode isolation is presented. A series of reads is applied to the crossbar before each write that is able to determine the resistance of each memristor in the crossbar despite the many parallel resistance paths. This is essential because the variation observed in memristor crossbars makes programming very difficult when using just a single write pulse and no error checking. A neuromorphic circuit is programmed using this method. Results show successful ex-situ training of a high-density crossbar with significant area savings when compared with a one transistor one memristor design.