Exploiting machine learning for bestowing intelligence to microfluidics.

Intelligent microfluidics is an emerging cross-discipline research area formed by combining microfluidics with machine learning. It uses the advantages of microfluidics, such as high throughput and controllability, and the powerful data processing capabilities of machine learning, resulting in improved systems in biotechnology and chemistry. Compared to traditional microfluidics using manual analysis methods, intelligent microfluidics needs less human intervention, and results in a more user-friendly experience with faster processing. There is a paucity of literature reviewing this burgeoning and highly promising cross-discipline. Therefore, we herein comprehensively and systematically summarize several aspects of microfluidic applications enabled by machine learning. We list the types of microfluidics used in intelligent microfluidic applications over the last five years, as well as the machine learning algorithms and the hardware used for training. We also present the most recent advances in key technologies, developments, challenges, and the emerging opportunities created by intelligent microfluidics.

Self-rectifying resistive memory in passive crossbar arrays.

Conventional computing architectures are poor suited to the unique workload demands of deep learning, which has led to a surge in interest in memory-centric computing. Herein, a trilayer (Hf0.8Si0.2O2/Al2O3/Hf0.5Si0.5O2)-based self-rectifying resistive memory cell (SRMC) that exhibits (i) large selectivity (ca. 104), (ii) two-bit operation, (iii) low read power (4 and 0.8 nW for low and high resistance states, respectively), (iv) read latency (<10 µs), (v) excellent non-volatility (data retention >104 s at 85 °C), and (vi) complementary metal-oxide-semiconductor compatibility (maximum supply voltage ≤5 V) is introduced, which outperforms previously reported SRMCs. These characteristics render the SRMC highly suitable for the main memory for memory-centric computing which can improve deep learning acceleration. Furthermore, the low programming power (ca. 18 nW), latency (100 µs), and endurance (>106) highlight the energy-efficiency and highly reliable random-access memory of our SRMC. The feasible operation of individual SRMCs in passive crossbar arrays of different sizes (30 × 30, 160 × 160, and 320 × 320) is attributed to the large asymmetry and nonlinearity in the current-voltage behavior of the proposed SRMC, verifying its potential for application in large-scale and high-density non-volatile memory for memory-centric computing.

Transparent amorphous strontium titanate resistive memories with transient photo-response.

Transparent non-volatile memory devices are desirable for realizing visually-clear integrated systems for information storage. Optical transparency provides advantages in applications such as smart glass electronic devices and wearable electronics. However, achieving high transparency limits the choice of active layers as well as the electrodes; thereby, constraining device processing and performance. Here, we demonstrate bilayer transparent memory cells using room temperature deposited amorphous strontium titanate as the functional material and indium tin oxide electrodes. The entire device is fabricated on glass, making the system highly transparent (>85%) in the visible spectrum. The devices exhibit switching ratios of over two orders of magnitude with measured retention of 105 s and endurance 104 cycles. Through the cross-sectional microstructural analyses it is shown that the asymmetric interfaces and distribution of oxygen vacancies in the bilayer oxide stack are responsible for defining the bipolar resistive switching behaviors. A photoluminescence mapping technique is employed to map the evolution of oxygen vacancies and pinpoint the location of the conductive filament. A transient response to optical excitation (using UV and blue light) is demonstrated in the high resistance state which indicates their potential as multifunctional memories for future transparent electronics.

Microstructure and dynamics of vacancy-induced nanofilamentary switching network in donor doped SrTiO3-x memristors.

Donor doping of perovskite oxides has emerged as an attractive technique to create high performance and low energy non-volatile analog memories. Here, we examine the origins of improved switching performance and stable multi-state resistive switching in Nb-doped oxygen-deficient amorphous SrTiO3 (Nb:a-STO x ) metal-insulator-metal (MIM) devices. We probe the impact of substitutional dopants (i.e., Nb) in modulating the electronic structure and subsequent switching performance. Temperature stability and bias/time dependence of the switching behavior are used to ascertain the role of substitutional dopants and highlight their utility to modulate volatile and non-volatile behavior in a-STO x devices for adaptive and neuromorphic applications. We utilized a combination of transmission electron microscopy, photoluminescence emission properties, interfacial compositional evaluation, and activation energy measurements to investigate the microstructure of the nanofilamentary network responsible for switching. These results provide important insights into understanding mechanisms that govern the performance of donor-doped perovskite oxide-based memristive devices.