Operation mechanism and efficiency-limiting factors in solution-processed quantum-dots light-emitting diodes

Abstract

Efficiency limiting factors of quantum dots light-emitting diodes (QLEDs) are studied by means of a machine learning approach using features taken from published data. Prototypical structure of QLEDs studied here is transparent conductive oxide (TCO) (anode)/hole injection layer (HIL)/hole transport layer (HTL)/quantum dots (QDs)/electron transport layer (ETL)/Al (cathode), which can be fabricated by printing processes. The most important efficiency limiting factor manifested from the machine learning is the hole injection barrier from HTL to QD layer, and the energy barrier reported in literature is about 1 eV in CdSe QDs based QLEDs. A mechanism of the hole injection in such QLEDs is examined by means of device simulation. Other efficiency limiting factors - the electron mobility of HTL and carrier balance in QD layer - are shown on the basis of the device simulation results. The importance of the efficiency limiting factors are experimentally confirmed, indicating that information concerning the electronic transport properties of HTL, QD layer and ETL is essential for the design of QLEDs by means of machine learning approach.