Effect of Gaussian defect density variations on electrical characteristics of TIPS-pentacene-based OTFT

This paper presents the influence of changes in the density of deep (Gaussian) defects, their energetic position, and width on key electrical parameters, including threshold voltage, current on–off ratio, and maximum transconductance in TIPS-pentacene-based organic thin-film transistors (OTFTs). Due to intrinsic disorder, organic semiconductors function with a Gaussian density of states governing the movement and injection of charge carriers within these materials. Our study reveals the presence of deep acceptor and donor density of states within the band gap of the TIPS-pentacene can significantly affect the performance of OTFTs. When the Gaussian acceptor ( \(N_{\textrm{GA}}\) $N_{\text{GA}}$ ) value is \(1\times 10^{15}\,{\textrm{cm}}^{-3}\,{\textrm{eV}}^{-1}\) $1\times {10}^{15}{\text{cm}}^{-3}{\text{eV}}^{-1}$ , the current on–off ratio ( \(I_{\textrm{on}}/I_{\textrm{off}}\) $I_{\text{on}}/I_{\text{off}}$ ) is at its peak, reaching \(2.3\times 10^7\) $2.3\times {10}^7$ , and the mobility is notably high at \(0.0270\, {\textrm{cm}}^{2}\,{\textrm{V}}^{-1}\,{\textrm{S}}^{-1}\) $0.0270{\text{cm}}^2{\text{V}}^{-1}{\text{S}}^{-1}$ . In the case of the Gaussian donor ( \(N_{\textrm{GD}}\) $N_{\text{GD}}$ ) with a value of \(1\times 10^{17}\,{\textrm{cm}}^{-3}\,{\textrm{eV}}^{-1}\) $1\times {10}^{17}{\text{cm}}^{-3}{\text{eV}}^{-1}$ , the current on–off ratio ( \(I_{\textrm{on}}/I_{\textrm{off}}\) $I_{\text{on}}/I_{\text{off}}$ ) reaches its peak at \(7.9\times 10^7\) $7.9\times {10}^7$ , and the lowest threshold voltage ( \(V_{\textrm{th}}\) $V_{\text{th}}$ ) is at 1.26 V. For the acceptor-like Gaussian decay energy ( \(W_{\textrm{GA}}\) $W_{\text{GA}}$ ) with a value of 0.1 eV, the current on–off ratio ( \(I_{\textrm{on}}/I_{\textrm{off}}\) $I_{\text{on}}/I_{\text{off}}$ ) peaks at \(2.4\times 10^5\) $2.4\times {10}^5$ . The dynamic control of charge trapping in this context holds the potential for various applications, including memory-related functions and the emulation of neurons in neuromorphic circuits for deep learning and artificial intelligence.