Tunneling‐Effect‐Boosted Interfacial Charge Trapping toward Photo‐Organic Transistor Memory

Abstract

Controlling exciton dynamics at organic semiconductor interfaces is of fundamental and practical importance, for example, in an organic field‐effect transistor (OFET) photomemory that possesses multifunctional applications. Moreover, the intrinsic properties of embedded charge storage layer may limit the device performances. This report introduces a concept of tunneling‐effect‐assisted interfacial charge trapping and takes full benefit of inherent rich surface traps of dielectrics for OFET photomemory. By controlling the thickness of barrier interlayer between active channel and dielectric, the trapped charge injection and dissipation can be regulated precisely. As a result, excellent transistor characteristics display an inconspicuous threshold voltage shift and hysteresis in dark or light exposure, while the same device can exhibit an outstanding memory behavior upon a gate bias pulse in bright situation. Without any additional materials handling such as photoactive charge storage layer into the construction, it can promise a low‐cost, facile, and versatile device fabrication.

Influence of Molecular Weight of Polymer Electret on the Synaptic Organic Field‐Effect Transistor Performance

“…And the grain boundaries of semiconductors appeared to play a minor role in vertical charge injection processes. [36] Therefore, the possibility of interfacial factors on the performance of the synaptic OFETs induced by polymer Mw changes should be dominantly dependent on surface morphology.…”

A brief review on device operations and working mechanisms of organic transistor photomemories

“…12, a widely-used conventional OFET that constitutes a pentacene semiconducting channel on a silicon wafer with the octadecyltrimethoxysilane (OTS)-modified SiO 2 layer can operate reversibly between the normal transistor and memory transistor devices flawlessly. 8 Following this peculiar case, the authors highlighted that the photogenerated excitons and tunneling effect played a significant role in manifesting the photomemory behavior. In short, the photogenerated excitons stem from the semiconductor active channel upon illumination, whilst, the tunneling effect occurs at the same time when a sufficiently large electric field exists, driving the respective free charges (in the semiconducting channel) to overcome the tunneling barrier potential and get trapped (see Fig.…”

“…Interestingly, aside from exposure time, the light properties that illuminate the photonic memory device also strongly affect the memory behavior, for instance, different light wavelengths (photon energy) and light intensities/powers. 39 Hence, this phenomenon ignites another important parameter named “photosensitivity” 8 and “photoresponsivity”. 36 Although, both photosensitivity and photoresponsivity are defined as the changing portion of output I d due to light irradiation, yet, the photoresponsivity may offer a better comparison study since the changes in output I d are normalized by the incident illumination power.…”

“…As mentioned in the introduction section, many promising applications in the rapid growth of IoT 4 and AI 5,6 have been realized by adopting the concept of device operations and mechanisms of transistor photomemory. [7][8][9][10][11][12][13][14][15][16][17] Among the plethora of applications, in this section, the discussion will focus on two crucial applications, there are multilevel storage memory cells 7,8 and artificial synapses 10,11 due to the similarity of working principle utilization; 91 for other fascinating and feasible applications based on photonic memory devices can be found in the recent reports and literature reviews. 20,30,44,50,[92][93][94][95][96][97]…”

“…As information technology has rapidly progressed, nowadays, many innovations in electronic components have also been developed specifically in response to the fields of the Internet of Things (IoT) and artificial intelligence (AI). [4][5][6] For instance, a high-density data storage, 7,8 a pulse monitoring sensor, 9 an artificial synapse, 10,11 a smart artificial retina, 12 a photorecorder, 13,14 pattern/image identification, 15,16 and an ultrasensitive photodetector; 17 all have been built-up based on a memory transistor framework. The clear difference between a memory transistor and a conventional one is in at least one additional polarization/charge storage layer that is sandwiched within the semiconductor and the gate contact (see Fig.…”