{"title":"用于短波红外光探测器的神经网络模型 PbS 胶体量子点合成指南","authors":"","doi":"10.1016/j.optmat.2024.116069","DOIUrl":null,"url":null,"abstract":"<div><p>PbS colloidal quantum dots (CQDs) have important applications in short-wave infrared (SWIR) detection due to its wide tunable bandgap, low thermoelectric noise, and solution processing capability. Due to the exciton peak of QDs determines the response band of the detector, while QDs with good monodispersed often exhibit better optical performance in photodetectors. The detection performance of PbS CQD-based SWIR photodetectors is closely related to the synthetic properties of QDs in the active layer. In addition, the emergence of machine learning in recent years has accelerated the exploration of QDs synthesis processes. Here, a framework is developed by neural network model which can learn from existing experimental data, through proposed experimental parameters for try, and ultimately point to regions of synthetic parameter space, thereby rapidly and accurately predicting the exciton peak and peak/valley ratio of synthesized CQDs. In terms of model performance, the NN model achieved a correlation coefficient of 0.93 for exciton peak prediction, which is very close to 1. For peak/valley ratio prediction, the correlation coefficient reached 0.75. In prediction of the latest synthesized CQD, the prediction error of exciton peak is only 3.89 %, and the prediction error of peak/valley ratio is 7.24 %. Furthermore, this batch of well synthesized monodisperse CQDs with a peak/valley ratio of 3.105 were used to prepare SWIR photoconductive devices, which demonstrates an excellent device performance, with the responsivity achieving 2.53 A/W, the detectivity reaching up to 2.08 × 10<sup>12</sup> Jones and the noise current of only 7.81 × 10<sup>−13</sup> A/Hz<sup>1/2</sup>. This work provides an effective method for preparing PbS CQD of various waveband with uniform particle size, which is expected to reduce costs for high-performance SWIR photodetectors.</p></div>","PeriodicalId":19564,"journal":{"name":"Optical Materials","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Synthesis guidance of PbS colloidal quantum dots with neural network model for short wave infrared photodetector\",\"authors\":\"\",\"doi\":\"10.1016/j.optmat.2024.116069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>PbS colloidal quantum dots (CQDs) have important applications in short-wave infrared (SWIR) detection due to its wide tunable bandgap, low thermoelectric noise, and solution processing capability. Due to the exciton peak of QDs determines the response band of the detector, while QDs with good monodispersed often exhibit better optical performance in photodetectors. The detection performance of PbS CQD-based SWIR photodetectors is closely related to the synthetic properties of QDs in the active layer. In addition, the emergence of machine learning in recent years has accelerated the exploration of QDs synthesis processes. Here, a framework is developed by neural network model which can learn from existing experimental data, through proposed experimental parameters for try, and ultimately point to regions of synthetic parameter space, thereby rapidly and accurately predicting the exciton peak and peak/valley ratio of synthesized CQDs. In terms of model performance, the NN model achieved a correlation coefficient of 0.93 for exciton peak prediction, which is very close to 1. For peak/valley ratio prediction, the correlation coefficient reached 0.75. In prediction of the latest synthesized CQD, the prediction error of exciton peak is only 3.89 %, and the prediction error of peak/valley ratio is 7.24 %. Furthermore, this batch of well synthesized monodisperse CQDs with a peak/valley ratio of 3.105 were used to prepare SWIR photoconductive devices, which demonstrates an excellent device performance, with the responsivity achieving 2.53 A/W, the detectivity reaching up to 2.08 × 10<sup>12</sup> Jones and the noise current of only 7.81 × 10<sup>−13</sup> A/Hz<sup>1/2</sup>. This work provides an effective method for preparing PbS CQD of various waveband with uniform particle size, which is expected to reduce costs for high-performance SWIR photodetectors.</p></div>\",\"PeriodicalId\":19564,\"journal\":{\"name\":\"Optical Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optical Materials\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0925346724012527\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optical Materials","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925346724012527","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Synthesis guidance of PbS colloidal quantum dots with neural network model for short wave infrared photodetector
PbS colloidal quantum dots (CQDs) have important applications in short-wave infrared (SWIR) detection due to its wide tunable bandgap, low thermoelectric noise, and solution processing capability. Due to the exciton peak of QDs determines the response band of the detector, while QDs with good monodispersed often exhibit better optical performance in photodetectors. The detection performance of PbS CQD-based SWIR photodetectors is closely related to the synthetic properties of QDs in the active layer. In addition, the emergence of machine learning in recent years has accelerated the exploration of QDs synthesis processes. Here, a framework is developed by neural network model which can learn from existing experimental data, through proposed experimental parameters for try, and ultimately point to regions of synthetic parameter space, thereby rapidly and accurately predicting the exciton peak and peak/valley ratio of synthesized CQDs. In terms of model performance, the NN model achieved a correlation coefficient of 0.93 for exciton peak prediction, which is very close to 1. For peak/valley ratio prediction, the correlation coefficient reached 0.75. In prediction of the latest synthesized CQD, the prediction error of exciton peak is only 3.89 %, and the prediction error of peak/valley ratio is 7.24 %. Furthermore, this batch of well synthesized monodisperse CQDs with a peak/valley ratio of 3.105 were used to prepare SWIR photoconductive devices, which demonstrates an excellent device performance, with the responsivity achieving 2.53 A/W, the detectivity reaching up to 2.08 × 1012 Jones and the noise current of only 7.81 × 10−13 A/Hz1/2. This work provides an effective method for preparing PbS CQD of various waveband with uniform particle size, which is expected to reduce costs for high-performance SWIR photodetectors.
期刊介绍:
Optical Materials has an open access mirror journal Optical Materials: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review.
The purpose of Optical Materials is to provide a means of communication and technology transfer between researchers who are interested in materials for potential device applications. The journal publishes original papers and review articles on the design, synthesis, characterisation and applications of optical materials.
OPTICAL MATERIALS focuses on:
• Optical Properties of Material Systems;
• The Materials Aspects of Optical Phenomena;
• The Materials Aspects of Devices and Applications.
Authors can submit separate research elements describing their data to Data in Brief and methods to Methods X.