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  1. We characterized resistance drift in phase change memory devices in the 80 K to 300 K temperature range by performing measurements on 20 nm thick, ∼70–100 nm wide lateral Ge2Sb2Te5(GST) line cells. The cells were amorphized using 1.5–2.5 V pulses with ∼50–100 ns duration leading to ∼0.4–1.1 mA peak reset currents resulting in amorphized lengths between ∼50 and 700 nm. Resistance drift coefficients in the amorphized cells are calculated using constant voltage measurements starting as fast as within a second after amorphization and for 1 h duration. Drift coefficients range between ∼0.02 and 0.1 with significant device-to-device variability and variations during the measurement period. At lower temperatures (higher resistance states) some devices show a complex dynamic behavior, with the resistance repeatedly increasing and decreasing significantly over periods in the order of seconds. These results point to charge trapping and de-trapping events as the cause of resistance drift.

     
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    Free, publicly-accessible full text available February 1, 2025
  2. We model the current density in a semiconductor based on the drift-diffusion transport of the charge carriers to accurately determine the thermoelectric effects in the bulk material (Thomson effect) and material junctions (Peltier effect). We utilize the model to perform 2-D finite element simulations of mushroom phase change memory cell with a critical dimension of 20 nm using temperature and electric field dependent material parameters and analyze the contributions of symmetric Joule heating and asymmetric thermoelectric heats during reset and set operations. We investigate the effect of altering the direction of current flow by changing the connection point between the cell and the access device and observe that, corresponding change in thermoelectric effects cause significant difference in operation dynamics, temperature distribution profiles, amorphous volume, energy requirement and resistance contrast between reset and set states. 
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  3. We calculate critical electronic conduction parameters of the amorphous phase of Ge 2 Sb 2 Te 5 (GST), a common material used in phase change memory. We estimate the room temperature bandgap of metastable amorphous GST to be E g (300K) = 1.84 eV based on a temperature dependent energy band model. We estimate the free carrier concentration at the melting temperature utilizing the latent heat of fusion to be 1.47 x 10 22 cm -3 . Using the thin film melt resistivity, we calculate the carrier mobility at melting point as 0.187 cm 2 /V-s. Assuming that metastable amorphous GST is a supercooled liquid with bipolar conduction, we compute the total carrier concentration as a function of temperature and estimate the room temperature free carrier concentration as p(300K) ≈ n(300K) = 1.69×10 17 cm -3 . Free electrons and holes are expected to recombine over time and the stable (drifted) amorphous GST is estimated to have p-type conduction with p(300K) ≈ 6×10 16 cm -3 . 
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  4. null (Ed.)
    Investigating the earliest stages of crystallization requires the transmission electron microscope (TEM) and is particularly challenging for materials which can be affected by the electron beam. Typically, when imaging at magnifications high enough to observe local crystallinity, the electron beam's current density must be high to produce adequate image contrast. Yet, minimizing the electron dose is necessary to reduce the changes caused by the beam. With the advent of a sensitive, high-speed, direct-detection camera for a TEM that is corrected for spherical aberration, it is possible to probe the early stages of crystallization at the atomic scale. High-quality images with low contrast can now be analyzed using new computing methods. In the present paper, this approach is illustrated for crystallization in a Ge 2 Sb 2 Te 5 (GST-225) phase-change material which can undergo particularly rapid phase transformations and is sensitive to the electron beam. A thin (20 nm) film of GST-225 has been directly imaged in the TEM and the low-dose images processed using Python scripting to extract details of the nanoscale nuclei. Quantitative analysis of the processed images in a video sequence also allows the growth of such nuclei to be followed. 
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