Related data for the paper: Response estimation and evaluation of direct-conversion dual-layer perovskite X-ray detectors: a numerical study with a cascaded signal model

This dataset contains numerical simulation results analyzing the imaging performance of dual-layer perovskite-based flat-panel X-ray detectors using methylammonium lead iodide (MAPbI₃) as the material. The simulations were conducted using a cascaded linear systems model, evaluating three key metrics: 1. Sensitivity, 2.Modulation Transfer Function (MTF), 3. Detective Quantum Efficiency (DQE). The results are stored as multi-dimensional arrays in MATLAB (.mat) files and are categorized based on different detector parameters. Specifically, the data are used to generate Fig. 3-11. 1.Sensitivity dataFile naming: Sen_RQA[5/7/9]Mat_PbI3.matKey Variables- Top layer: S1(dim1:spectrum,dim2:Lt/L,dim3:SC or PC,dim4:ElectricField)) - Bottom layer: S2(dim1:spectrum,dim2:Lt/L,dim3:SC or PC,dim4:ElectricField)Dimension description：- dim1=1;- dim2=1,2,3,4,5 corresponds to Lt/L=10%,20%,30%,40%,50%, respectively ;- dim3=1,2 corresponds to SC and PC, respectively;- dim4=1,2,3,4,5 corresponds to F=0.01,0.05,0.1,0.5,1 V/um, respectively. Examples: Fig3(a), plot Sensitivity_vs_F, RQA7, Lt/L:10%-50%, SC> Load(‘Sen_RQA7Mat_PbI3.mat’)> plot(F(:),S1(1,j,1,:));%% top layer, j=1,2,3,4,5> plot(F(:),S2(1,j,1,:));%% bottom layer, j=1,2,3,4,5 2. MTF dataFile naming: MTF_RQA5Mat_PbI3.mat, MTF_RQA7Mat_PbI3.mat, MTF_RQA9Mat_PbI3.matKey Variable-Top layer: MTF_t(dim1:frequency,dim2:Lt/L,dim3: ElectricField, dim4:SC or PC) -Bottom layer: MTF_b(dim1:frequency,dim2:Lt/L,dim3: ElectricField, dim4:SC or PC) Dimension description：- dim1=1:1:100 corresponds to f=1:0.1:10, respectively;- dim2=1,2,3,4,5 corresponds to Lt/L=10%,20%,30%,40%,50%, respectively; - dim3=1,2,3,4,5 corresponds to F=0.01,0.05,0.1,0.5,1 V/um, respectively;- dim4=1,2 corresponds to SC and PC, respectively Examples: Fig7(a), plot MTF_vs_frequency, Lt/L=30%, SC, F=0.1 V/um> Load(‘MTF_RQA5Mat_PbI3.mat’), plot(f(:)./2,MTF_t(:,3,3,1)); %% top layer,RQA5> Load(‘MTF_RQA7Mat_PbI3.mat’), plot(f(:)./2,MTF_t(:,3,3,1));%% top layer,RQA7 > Load(‘MTF_RQA9Mat_PbI3.mat’), plot(f(:)./2,MTF_t(:,3,3,1));%% top layer,RQA9 3. DQE dataFile naming: DQE_RQA5_D0.1.mat, DQE_RQA5_D1.mat, DQE_RQA7_D0.1.mat, DQE_RQA7_D1.mat, DQE_RQA9_D0.1.mat, DQE_RQA9_D1.matKey Variable- Top layer: dqe_t(dim1:frequency,dim2:Lt/L,dim3: ElectricField, dim4:SC or PC,dim5: eNoise, dim6: dark current) - Bottom layer: dqe_b(dim1:frequency,dim2:Lt/L,dim3: ElectricField, dim4:SC or PC,dim5: eNoise, dim6: dark current) Dimension description：- dim1=1:1:100 corresponds to f=1:0.1:10, respectively;- dim2=1,2,3,4,5 corresponds to Lt/L=10%,20%,30%,40%,50%, respectively; - dim3=1,2,3,4,5 corresponds to F=0.01,0.05,0.1,0.5,1 V/um, respectively;- dim4=1,2 corresponds to SC and PC, respectively;- dim5=1,2,3 corresponds to eNoise=200, 700, 2000e-, respectively;- dim6=1,2,3,4,5, corresponds to dark current eq7.Jd(F)) Examples: Fig11(a), plot DQE_vs_frequency, Lt/L=30%, PC, RQA7, F=0.5 V/um, 1uGy> Load(‘DQE_RQA7_D1.mat’);> plot(f(:)./2,dqe_t(:,3,4,2,i,4)); %% top layer,i =1,2,3> plot(f(:)./2,dqe_b(:,3,4,2,i,4)); %% bottom layer, i=1,2,3