Physical reservoir computing (PRC) attracts considerable attention as a promising candidate to achieve high-performance and low-power machine learning (ML) at the edge region. We have focused on the electrochemical reactions of metal cations in ionic liquids (ILs), which derive fundamental functionalities as a physical reservoir device (PRD) [1], [2] between the input and read out layers in the ML model of the PRC. To improve the implementability of our electrochemical PRD, we developed the ionogel (IG) which are principally composed of the SiO₂ nanoparticles and the IL containing Cu cations. IL used in the present study was 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)amide ([C₄mim][Tf₂N]). By using the reverse-offset printing technology [3], we prepared a flat IG layer on microfabricated electrode patterns and obtained the crossbar-type ionogel-based PRD (IGPRD) as shown in Fig. 1(a). The thickness of the IG layer was approximately 200 nm, while the line width of the top and bottom electrodes (TE and BE) in Fig. 1(a) was approximately 100 mm. By changing the line width of TE and BE, it was confirmed that the output currents from IGPRD scaled with the electrode area, indicating that the electrochemical reactions at the interface between IG and the planar section of the electrodes rather than the peripheral part of the electrodes generate the output currents. We evaluated the short-term memory (STM) property of the present IGPRD by applying the pseudo-random binary sequence as the triangular voltage pulse (TVP) train. The signals 0 and 1 were represented as the negative and positive TVP, respectively. Fig. 1(b) is the current-voltage (I-V) curves color-coded into eight classes according to the three-digit number corresponding to the history of the input signals 0 and 1 at three continuous timesteps. The pulse width (PW) of the TVP was set to 500 ms. The results in Fig. 1(b) clearly show that different current waveforms were generated by the IGPRD depending on the history of the input signals, indicating that the IGPRD has the STM property. The amount of the STM in the IGPRD is quantified as a memory capacity (MC) in the STM task [2]. The value of MC over the time delays from 1 to 4 timesteps was approximately 2.24 in the test phase, which is almost comparable to that for IL (MC = 2.39) evaluated using the planar-type PRD used in our previous study [1], [2]. The similar STM performance in the IGPRD was confirmed within the wide range of the PW conditions ranging from 50 ms to 500 ms. Therefore, the implementability of the electrochemical PRD was successfully improved by using IG patterns instead of IL, with keeping the STM task capability.