additional analog converters, imposing issues with scalability and power consumption. [2][3][4][5] Development of next-generation materials and devices for neuromorphic electronics entails detailed understanding of the fundamental device characteristics and their possible emulation capabilities at an elemental level. Ionically gated transistors harness diffusive mechanics to achieve continuous modulation of channel conductance at low-power, but require coupling of two disparate electronically and ionically active material sets. [6,7] Solutions based on drift-memristors are inherently disadvantaged due to digital-like abrupt switching transitions, which limit their plasticity. [8] Very recently, second-order drift memristors, [9,10] electrochemical metallization cells, [11] and diffusive memristors [8] have been engineered to approximate the biological Ca 2+ dynamics based on metal atom diffusion, thermal dissipation, [9] mobility decay, [12] and spontaneous nanoparticle formation, but often require additional nonvolatile elements in series for long-term memory storage. An ionic semiconductor which intimately combines rapid electronic transitions with slow drift-diffusive ionic kinetics will enable dynamic tuning of metastable memristive conductance states, allowing efficient emulation of synaptic characteristics and catering for novel low-power architectures that exploit electronic properties of the semiconductor.Emulation of brain-like signal processing is the foundation for development of efficient learning circuitry, but few devices offer the tunable conductance range necessary for mimicking spatiotemporal plasticity in biological synapses. An ionic semiconductor which couples electronic transitions with drift-diffusive ionic kinetics would enable energy-efficient analog-like switching of metastable conductance states. Here, ionic-electronic coupling in halide perovskite semiconductors is utilized to create memristive synapses with a dynamic continuous transition of conductance states. Coexistence of carrier injection barriers and ion migration in the perovskite films defines the degree of synaptic plasticity, more notable for the larger organic ammonium and formamidinium cations than the inorganic cesium counterpart. Optimized pulsing schemes facilitates a balanced interplay of short-and longterm plasticity rules like paired-pulse facilitation and spike-time-dependent plasticity, cardinal for learning and computing. Trained as a memory array, halide perovskite synapses demonstrate reconfigurability, learning, forgetting, and fault tolerance analogous to the human brain. Network-level simulations of unsupervised learning of handwritten digit images utilizing experimentally derived device parameters, validates the utility of these memristors for energy-efficient neuromorphic computation, paving way for novel ionotronic neuromorphic architectures with halide perovskites as the active material. Artificial SynapsesThe ORCID identification number(s) for the author(s) of this article can be found under https://doi.C...

Online, real-time learning in neuromorphic circuits have been implemented through variants of Spike Time Dependent Plasticity (STDP). Current implementations have used either floating-gate devices or memristors to implement such learning synapses together with non-volatile storage. However, these approaches require high voltages (≈ 3 − 12V) for weight update and entail high energy for learning (≈ 4 − 30pJ/write). We present a domain wall memory based low-voltage, low-energy STDP synapse that can operate with a power supply as low as 0.8V and update the weight at ≈ 40fJ/write. Device level simulations are performed to prove its feasibility. Its use in associative learning is also demonstrated by using neurons with dendritic branches to classify spike patterns from MNIST dataset.

We present a novel readout circuit for a ferromagnetic Hall cross-bar based random number generator. The random orientation of magnetic domains are result of anomalous Hall-effect. These ferromagnetic Hall cross-bar structures can be integrated with the read out circuit to form a plug and play random number generator. The system can resolve up to 15-20 µV Hall-voltages from Hall probe. Application of current densities around 10 12 A/m 2 through the Ferromagnetic Hall cross-bar produces random Hall-voltage on the output terminals. To amplify the weak Hall-voltages (10-100 µV) in the presence of DC offsets, a modulation scheme is used to up-convert the signal and a band-pass amplifier is used to amplify the modulated signal. The band-pass amplifier circuit, motivated by neural recording amplifier is designed in 65nm CMOS and consumes 126 µW of power from a 1.2 V supply. Further, we present a successive approximation algorithm and its embedded implementation to set the desired threshold for digitizing the amplified Hall-voltage in presence of signal drift. Experimental results show that the resulting system can tolerate drifts in voltage up to 440 µV. I. INTRODUCTIONRandom bit streams find application in generating keys in cryptography and initialization of parameters in a encrypted communication protocols. Random number generators are useful in realising Physically Unclonable Function (PUF) in microprocessors [1], [2]. These streams are also useful in stochastic simulations, gaming and events where random sampling is required. Random number generators can be divided into two classes based on their source, namely true random number generator (TRNG) and pseudo random number generator (PRNG). TRNG generates randomness from inherent stochastic physical feature of the source. On the other hand PRNG generates lengthy stream of digital bits which are difficult to predict. Most on-chip TRNG of present day use techniques like sampling of thermal noise [3] or exploiting meta stability of latching circuits [4]. Recently, magnetic random number generators (MRNG) have been proposed which *Corresponding Author is Arindam Basu. Equal contribution of Govind and Joydeep.

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I would like to express my sincere gratitude to my supervisor, Assoc. Prof. Arindam Basu, for his patient guidance and helpful advices, without which this thesis could never have been completed. I am grateful to Assoc. Prof. Chang Chip Hong for giving me an opportunity to work and learn in this interdisciplinary project. I aslo acknowledge the financial support for this work, which was provided by Ministry of Education, Singapore through grant MOE2013-T2-2-017. I am thankful to research scholars Dr.